BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification
This paper proposes an ensemble algorithm named of BPSO-Adaboost-KNN to cope with multi-class imbalanced data classification. The main idea of this algorithm is to integrate feature selection and boosting into ensemble. What’s more, we utilize a novel evaluation metric called AUCarea which is especi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Haixiang, Guo [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2016transfer abstract |
|---|
| Schlagwörter: |
|---|
| Umfang: |
18 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation - Liu, Xiang ELSEVIER, 2015, the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:49 ; year:2016 ; pages:176-193 ; extent:18 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.engappai.2015.09.011 |
|---|
| Katalog-ID: |
ELV029894786 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV029894786 | ||
| 003 | DE-627 | ||
| 005 | 20230625175812.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 180603s2016 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.engappai.2015.09.011 |2 doi | |
| 028 | 5 | 2 | |a GBVA2016016000011.pica |
| 035 | |a (DE-627)ELV029894786 | ||
| 035 | |a (ELSEVIER)S0952-1976(15)00211-0 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | |a 004 | |
| 082 | 0 | 4 | |a 004 |q DE-600 |
| 082 | 0 | 4 | |a 540 |q VZ |
| 082 | 0 | 4 | |a 610 |q VZ |
| 084 | |a 44.00 |2 bkl | ||
| 100 | 1 | |a Haixiang, Guo |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification |
| 264 | 1 | |c 2016transfer abstract | |
| 300 | |a 18 | ||
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a This paper proposes an ensemble algorithm named of BPSO-Adaboost-KNN to cope with multi-class imbalanced data classification. The main idea of this algorithm is to integrate feature selection and boosting into ensemble. What’s more, we utilize a novel evaluation metric called AUCarea which is especially for multi-class classification. In our model BPSO is employed as the feature selection algorithm in which AUCarea is chosen as the fitness. For classification, we generate a boosting classifier in which KNN is selected as the basic classifier. In order to verify the effectiveness of our method, 19 benchmarks are used in our experiments. The results show that the proposed algorithm improves both the stability and the accuracy of boosting after carrying out feature selection, and the performance of our algorithm is comparable with other state-of-the-art algorithms. In statistical analyses, we apply Bland–Altman analysis to show the consistencies between AUCarea and other popular metrics like average G-mean, average F-value etc. Besides, we use linear regression to find deeper correlation between AUCarea and other metrics in order to show why AUCarea works well in this issue. We also put out a series of statistical studies in order to analyze if there exist significant improvements after feature selection and boosting are employed. At last, the proposed algorithm is applied in oil-bearing of reservoir recognition. The classification precision is up to 99% in oilsk81-oilsk85 well logging data in Jianghan oilfield of China, which is 20% higher than KNN classifier. Particularly, the proposed algorithm has significant superiority when distinguishing the oil layer from other layers. | ||
| 520 | |a This paper proposes an ensemble algorithm named of BPSO-Adaboost-KNN to cope with multi-class imbalanced data classification. The main idea of this algorithm is to integrate feature selection and boosting into ensemble. What’s more, we utilize a novel evaluation metric called AUCarea which is especially for multi-class classification. In our model BPSO is employed as the feature selection algorithm in which AUCarea is chosen as the fitness. For classification, we generate a boosting classifier in which KNN is selected as the basic classifier. In order to verify the effectiveness of our method, 19 benchmarks are used in our experiments. The results show that the proposed algorithm improves both the stability and the accuracy of boosting after carrying out feature selection, and the performance of our algorithm is comparable with other state-of-the-art algorithms. In statistical analyses, we apply Bland–Altman analysis to show the consistencies between AUCarea and other popular metrics like average G-mean, average F-value etc. Besides, we use linear regression to find deeper correlation between AUCarea and other metrics in order to show why AUCarea works well in this issue. We also put out a series of statistical studies in order to analyze if there exist significant improvements after feature selection and boosting are employed. At last, the proposed algorithm is applied in oil-bearing of reservoir recognition. The classification precision is up to 99% in oilsk81-oilsk85 well logging data in Jianghan oilfield of China, which is 20% higher than KNN classifier. Particularly, the proposed algorithm has significant superiority when distinguishing the oil layer from other layers. | ||
| 650 | 7 | |a Imbalanced data |2 Elsevier | |
| 650 | 7 | |a Feature selection |2 Elsevier | |
| 650 | 7 | |a Ensemble |2 Elsevier | |
| 650 | 7 | |a Classification |2 Elsevier | |
| 650 | 7 | |a Oil reservoir |2 Elsevier | |
| 700 | 1 | |a Yijing, Li |4 oth | |
| 700 | 1 | |a Yanan, Li |4 oth | |
| 700 | 1 | |a Xiao, Liu |4 oth | |
| 700 | 1 | |a Jinling, Li |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Liu, Xiang ELSEVIER |t Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation |d 2015 |d the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control |g Amsterdam [u.a.] |w (DE-627)ELV013402978 |
| 773 | 1 | 8 | |g volume:49 |g year:2016 |g pages:176-193 |g extent:18 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.engappai.2015.09.011 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a SSG-OLC-PHA | ||
| 936 | b | k | |a 44.00 |j Medizin: Allgemeines |q VZ |
| 951 | |a AR | ||
| 952 | |d 49 |j 2016 |h 176-193 |g 18 | ||
| 953 | |2 045F |a 004 | ||
| author_variant |
g h gh |
|---|---|
| matchkey_str |
haixiangguoyijingliyananlixiaoliujinling:2016----:podbotnesmllannagrtmomlilsibln |
| hierarchy_sort_str |
2016transfer abstract |
| bklnumber |
44.00 |
| publishDate |
2016 |
| allfields |
10.1016/j.engappai.2015.09.011 doi GBVA2016016000011.pica (DE-627)ELV029894786 (ELSEVIER)S0952-1976(15)00211-0 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 VZ 610 VZ 44.00 bkl Haixiang, Guo verfasserin aut BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification 2016transfer abstract 18 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper proposes an ensemble algorithm named of BPSO-Adaboost-KNN to cope with multi-class imbalanced data classification. The main idea of this algorithm is to integrate feature selection and boosting into ensemble. What’s more, we utilize a novel evaluation metric called AUCarea which is especially for multi-class classification. In our model BPSO is employed as the feature selection algorithm in which AUCarea is chosen as the fitness. For classification, we generate a boosting classifier in which KNN is selected as the basic classifier. In order to verify the effectiveness of our method, 19 benchmarks are used in our experiments. The results show that the proposed algorithm improves both the stability and the accuracy of boosting after carrying out feature selection, and the performance of our algorithm is comparable with other state-of-the-art algorithms. In statistical analyses, we apply Bland–Altman analysis to show the consistencies between AUCarea and other popular metrics like average G-mean, average F-value etc. Besides, we use linear regression to find deeper correlation between AUCarea and other metrics in order to show why AUCarea works well in this issue. We also put out a series of statistical studies in order to analyze if there exist significant improvements after feature selection and boosting are employed. At last, the proposed algorithm is applied in oil-bearing of reservoir recognition. The classification precision is up to 99% in oilsk81-oilsk85 well logging data in Jianghan oilfield of China, which is 20% higher than KNN classifier. Particularly, the proposed algorithm has significant superiority when distinguishing the oil layer from other layers. This paper proposes an ensemble algorithm named of BPSO-Adaboost-KNN to cope with multi-class imbalanced data classification. The main idea of this algorithm is to integrate feature selection and boosting into ensemble. What’s more, we utilize a novel evaluation metric called AUCarea which is especially for multi-class classification. In our model BPSO is employed as the feature selection algorithm in which AUCarea is chosen as the fitness. For classification, we generate a boosting classifier in which KNN is selected as the basic classifier. In order to verify the effectiveness of our method, 19 benchmarks are used in our experiments. The results show that the proposed algorithm improves both the stability and the accuracy of boosting after carrying out feature selection, and the performance of our algorithm is comparable with other state-of-the-art algorithms. In statistical analyses, we apply Bland–Altman analysis to show the consistencies between AUCarea and other popular metrics like average G-mean, average F-value etc. Besides, we use linear regression to find deeper correlation between AUCarea and other metrics in order to show why AUCarea works well in this issue. We also put out a series of statistical studies in order to analyze if there exist significant improvements after feature selection and boosting are employed. At last, the proposed algorithm is applied in oil-bearing of reservoir recognition. The classification precision is up to 99% in oilsk81-oilsk85 well logging data in Jianghan oilfield of China, which is 20% higher than KNN classifier. Particularly, the proposed algorithm has significant superiority when distinguishing the oil layer from other layers. Imbalanced data Elsevier Feature selection Elsevier Ensemble Elsevier Classification Elsevier Oil reservoir Elsevier Yijing, Li oth Yanan, Li oth Xiao, Liu oth Jinling, Li oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:49 year:2016 pages:176-193 extent:18 https://doi.org/10.1016/j.engappai.2015.09.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 49 2016 176-193 18 045F 004 |
| spelling |
10.1016/j.engappai.2015.09.011 doi GBVA2016016000011.pica (DE-627)ELV029894786 (ELSEVIER)S0952-1976(15)00211-0 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 VZ 610 VZ 44.00 bkl Haixiang, Guo verfasserin aut BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification 2016transfer abstract 18 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper proposes an ensemble algorithm named of BPSO-Adaboost-KNN to cope with multi-class imbalanced data classification. The main idea of this algorithm is to integrate feature selection and boosting into ensemble. What’s more, we utilize a novel evaluation metric called AUCarea which is especially for multi-class classification. In our model BPSO is employed as the feature selection algorithm in which AUCarea is chosen as the fitness. For classification, we generate a boosting classifier in which KNN is selected as the basic classifier. In order to verify the effectiveness of our method, 19 benchmarks are used in our experiments. The results show that the proposed algorithm improves both the stability and the accuracy of boosting after carrying out feature selection, and the performance of our algorithm is comparable with other state-of-the-art algorithms. In statistical analyses, we apply Bland–Altman analysis to show the consistencies between AUCarea and other popular metrics like average G-mean, average F-value etc. Besides, we use linear regression to find deeper correlation between AUCarea and other metrics in order to show why AUCarea works well in this issue. We also put out a series of statistical studies in order to analyze if there exist significant improvements after feature selection and boosting are employed. At last, the proposed algorithm is applied in oil-bearing of reservoir recognition. The classification precision is up to 99% in oilsk81-oilsk85 well logging data in Jianghan oilfield of China, which is 20% higher than KNN classifier. Particularly, the proposed algorithm has significant superiority when distinguishing the oil layer from other layers. This paper proposes an ensemble algorithm named of BPSO-Adaboost-KNN to cope with multi-class imbalanced data classification. The main idea of this algorithm is to integrate feature selection and boosting into ensemble. What’s more, we utilize a novel evaluation metric called AUCarea which is especially for multi-class classification. In our model BPSO is employed as the feature selection algorithm in which AUCarea is chosen as the fitness. For classification, we generate a boosting classifier in which KNN is selected as the basic classifier. In order to verify the effectiveness of our method, 19 benchmarks are used in our experiments. The results show that the proposed algorithm improves both the stability and the accuracy of boosting after carrying out feature selection, and the performance of our algorithm is comparable with other state-of-the-art algorithms. In statistical analyses, we apply Bland–Altman analysis to show the consistencies between AUCarea and other popular metrics like average G-mean, average F-value etc. Besides, we use linear regression to find deeper correlation between AUCarea and other metrics in order to show why AUCarea works well in this issue. We also put out a series of statistical studies in order to analyze if there exist significant improvements after feature selection and boosting are employed. At last, the proposed algorithm is applied in oil-bearing of reservoir recognition. The classification precision is up to 99% in oilsk81-oilsk85 well logging data in Jianghan oilfield of China, which is 20% higher than KNN classifier. Particularly, the proposed algorithm has significant superiority when distinguishing the oil layer from other layers. Imbalanced data Elsevier Feature selection Elsevier Ensemble Elsevier Classification Elsevier Oil reservoir Elsevier Yijing, Li oth Yanan, Li oth Xiao, Liu oth Jinling, Li oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:49 year:2016 pages:176-193 extent:18 https://doi.org/10.1016/j.engappai.2015.09.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 49 2016 176-193 18 045F 004 |
| allfields_unstemmed |
10.1016/j.engappai.2015.09.011 doi GBVA2016016000011.pica (DE-627)ELV029894786 (ELSEVIER)S0952-1976(15)00211-0 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 VZ 610 VZ 44.00 bkl Haixiang, Guo verfasserin aut BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification 2016transfer abstract 18 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper proposes an ensemble algorithm named of BPSO-Adaboost-KNN to cope with multi-class imbalanced data classification. The main idea of this algorithm is to integrate feature selection and boosting into ensemble. What’s more, we utilize a novel evaluation metric called AUCarea which is especially for multi-class classification. In our model BPSO is employed as the feature selection algorithm in which AUCarea is chosen as the fitness. For classification, we generate a boosting classifier in which KNN is selected as the basic classifier. In order to verify the effectiveness of our method, 19 benchmarks are used in our experiments. The results show that the proposed algorithm improves both the stability and the accuracy of boosting after carrying out feature selection, and the performance of our algorithm is comparable with other state-of-the-art algorithms. In statistical analyses, we apply Bland–Altman analysis to show the consistencies between AUCarea and other popular metrics like average G-mean, average F-value etc. Besides, we use linear regression to find deeper correlation between AUCarea and other metrics in order to show why AUCarea works well in this issue. We also put out a series of statistical studies in order to analyze if there exist significant improvements after feature selection and boosting are employed. At last, the proposed algorithm is applied in oil-bearing of reservoir recognition. The classification precision is up to 99% in oilsk81-oilsk85 well logging data in Jianghan oilfield of China, which is 20% higher than KNN classifier. Particularly, the proposed algorithm has significant superiority when distinguishing the oil layer from other layers. This paper proposes an ensemble algorithm named of BPSO-Adaboost-KNN to cope with multi-class imbalanced data classification. The main idea of this algorithm is to integrate feature selection and boosting into ensemble. What’s more, we utilize a novel evaluation metric called AUCarea which is especially for multi-class classification. In our model BPSO is employed as the feature selection algorithm in which AUCarea is chosen as the fitness. For classification, we generate a boosting classifier in which KNN is selected as the basic classifier. In order to verify the effectiveness of our method, 19 benchmarks are used in our experiments. The results show that the proposed algorithm improves both the stability and the accuracy of boosting after carrying out feature selection, and the performance of our algorithm is comparable with other state-of-the-art algorithms. In statistical analyses, we apply Bland–Altman analysis to show the consistencies between AUCarea and other popular metrics like average G-mean, average F-value etc. Besides, we use linear regression to find deeper correlation between AUCarea and other metrics in order to show why AUCarea works well in this issue. We also put out a series of statistical studies in order to analyze if there exist significant improvements after feature selection and boosting are employed. At last, the proposed algorithm is applied in oil-bearing of reservoir recognition. The classification precision is up to 99% in oilsk81-oilsk85 well logging data in Jianghan oilfield of China, which is 20% higher than KNN classifier. Particularly, the proposed algorithm has significant superiority when distinguishing the oil layer from other layers. Imbalanced data Elsevier Feature selection Elsevier Ensemble Elsevier Classification Elsevier Oil reservoir Elsevier Yijing, Li oth Yanan, Li oth Xiao, Liu oth Jinling, Li oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:49 year:2016 pages:176-193 extent:18 https://doi.org/10.1016/j.engappai.2015.09.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 49 2016 176-193 18 045F 004 |
| allfieldsGer |
10.1016/j.engappai.2015.09.011 doi GBVA2016016000011.pica (DE-627)ELV029894786 (ELSEVIER)S0952-1976(15)00211-0 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 VZ 610 VZ 44.00 bkl Haixiang, Guo verfasserin aut BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification 2016transfer abstract 18 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper proposes an ensemble algorithm named of BPSO-Adaboost-KNN to cope with multi-class imbalanced data classification. The main idea of this algorithm is to integrate feature selection and boosting into ensemble. What’s more, we utilize a novel evaluation metric called AUCarea which is especially for multi-class classification. In our model BPSO is employed as the feature selection algorithm in which AUCarea is chosen as the fitness. For classification, we generate a boosting classifier in which KNN is selected as the basic classifier. In order to verify the effectiveness of our method, 19 benchmarks are used in our experiments. The results show that the proposed algorithm improves both the stability and the accuracy of boosting after carrying out feature selection, and the performance of our algorithm is comparable with other state-of-the-art algorithms. In statistical analyses, we apply Bland–Altman analysis to show the consistencies between AUCarea and other popular metrics like average G-mean, average F-value etc. Besides, we use linear regression to find deeper correlation between AUCarea and other metrics in order to show why AUCarea works well in this issue. We also put out a series of statistical studies in order to analyze if there exist significant improvements after feature selection and boosting are employed. At last, the proposed algorithm is applied in oil-bearing of reservoir recognition. The classification precision is up to 99% in oilsk81-oilsk85 well logging data in Jianghan oilfield of China, which is 20% higher than KNN classifier. Particularly, the proposed algorithm has significant superiority when distinguishing the oil layer from other layers. This paper proposes an ensemble algorithm named of BPSO-Adaboost-KNN to cope with multi-class imbalanced data classification. The main idea of this algorithm is to integrate feature selection and boosting into ensemble. What’s more, we utilize a novel evaluation metric called AUCarea which is especially for multi-class classification. In our model BPSO is employed as the feature selection algorithm in which AUCarea is chosen as the fitness. For classification, we generate a boosting classifier in which KNN is selected as the basic classifier. In order to verify the effectiveness of our method, 19 benchmarks are used in our experiments. The results show that the proposed algorithm improves both the stability and the accuracy of boosting after carrying out feature selection, and the performance of our algorithm is comparable with other state-of-the-art algorithms. In statistical analyses, we apply Bland–Altman analysis to show the consistencies between AUCarea and other popular metrics like average G-mean, average F-value etc. Besides, we use linear regression to find deeper correlation between AUCarea and other metrics in order to show why AUCarea works well in this issue. We also put out a series of statistical studies in order to analyze if there exist significant improvements after feature selection and boosting are employed. At last, the proposed algorithm is applied in oil-bearing of reservoir recognition. The classification precision is up to 99% in oilsk81-oilsk85 well logging data in Jianghan oilfield of China, which is 20% higher than KNN classifier. Particularly, the proposed algorithm has significant superiority when distinguishing the oil layer from other layers. Imbalanced data Elsevier Feature selection Elsevier Ensemble Elsevier Classification Elsevier Oil reservoir Elsevier Yijing, Li oth Yanan, Li oth Xiao, Liu oth Jinling, Li oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:49 year:2016 pages:176-193 extent:18 https://doi.org/10.1016/j.engappai.2015.09.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 49 2016 176-193 18 045F 004 |
| allfieldsSound |
10.1016/j.engappai.2015.09.011 doi GBVA2016016000011.pica (DE-627)ELV029894786 (ELSEVIER)S0952-1976(15)00211-0 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 VZ 610 VZ 44.00 bkl Haixiang, Guo verfasserin aut BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification 2016transfer abstract 18 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper proposes an ensemble algorithm named of BPSO-Adaboost-KNN to cope with multi-class imbalanced data classification. The main idea of this algorithm is to integrate feature selection and boosting into ensemble. What’s more, we utilize a novel evaluation metric called AUCarea which is especially for multi-class classification. In our model BPSO is employed as the feature selection algorithm in which AUCarea is chosen as the fitness. For classification, we generate a boosting classifier in which KNN is selected as the basic classifier. In order to verify the effectiveness of our method, 19 benchmarks are used in our experiments. The results show that the proposed algorithm improves both the stability and the accuracy of boosting after carrying out feature selection, and the performance of our algorithm is comparable with other state-of-the-art algorithms. In statistical analyses, we apply Bland–Altman analysis to show the consistencies between AUCarea and other popular metrics like average G-mean, average F-value etc. Besides, we use linear regression to find deeper correlation between AUCarea and other metrics in order to show why AUCarea works well in this issue. We also put out a series of statistical studies in order to analyze if there exist significant improvements after feature selection and boosting are employed. At last, the proposed algorithm is applied in oil-bearing of reservoir recognition. The classification precision is up to 99% in oilsk81-oilsk85 well logging data in Jianghan oilfield of China, which is 20% higher than KNN classifier. Particularly, the proposed algorithm has significant superiority when distinguishing the oil layer from other layers. This paper proposes an ensemble algorithm named of BPSO-Adaboost-KNN to cope with multi-class imbalanced data classification. The main idea of this algorithm is to integrate feature selection and boosting into ensemble. What’s more, we utilize a novel evaluation metric called AUCarea which is especially for multi-class classification. In our model BPSO is employed as the feature selection algorithm in which AUCarea is chosen as the fitness. For classification, we generate a boosting classifier in which KNN is selected as the basic classifier. In order to verify the effectiveness of our method, 19 benchmarks are used in our experiments. The results show that the proposed algorithm improves both the stability and the accuracy of boosting after carrying out feature selection, and the performance of our algorithm is comparable with other state-of-the-art algorithms. In statistical analyses, we apply Bland–Altman analysis to show the consistencies between AUCarea and other popular metrics like average G-mean, average F-value etc. Besides, we use linear regression to find deeper correlation between AUCarea and other metrics in order to show why AUCarea works well in this issue. We also put out a series of statistical studies in order to analyze if there exist significant improvements after feature selection and boosting are employed. At last, the proposed algorithm is applied in oil-bearing of reservoir recognition. The classification precision is up to 99% in oilsk81-oilsk85 well logging data in Jianghan oilfield of China, which is 20% higher than KNN classifier. Particularly, the proposed algorithm has significant superiority when distinguishing the oil layer from other layers. Imbalanced data Elsevier Feature selection Elsevier Ensemble Elsevier Classification Elsevier Oil reservoir Elsevier Yijing, Li oth Yanan, Li oth Xiao, Liu oth Jinling, Li oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:49 year:2016 pages:176-193 extent:18 https://doi.org/10.1016/j.engappai.2015.09.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 49 2016 176-193 18 045F 004 |
| language |
English |
| source |
Enthalten in Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation Amsterdam [u.a.] volume:49 year:2016 pages:176-193 extent:18 |
| sourceStr |
Enthalten in Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation Amsterdam [u.a.] volume:49 year:2016 pages:176-193 extent:18 |
| format_phy_str_mv |
Article |
| bklname |
Medizin: Allgemeines |
| institution |
findex.gbv.de |
| topic_facet |
Imbalanced data Feature selection Ensemble Classification Oil reservoir |
| dewey-raw |
004 |
| isfreeaccess_bool |
false |
| container_title |
Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation |
| authorswithroles_txt_mv |
Haixiang, Guo @@aut@@ Yijing, Li @@oth@@ Yanan, Li @@oth@@ Xiao, Liu @@oth@@ Jinling, Li @@oth@@ |
| publishDateDaySort_date |
2016-01-01T00:00:00Z |
| hierarchy_top_id |
ELV013402978 |
| dewey-sort |
14 |
| id |
ELV029894786 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV029894786</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230625175812.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180603s2016 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.engappai.2015.09.011</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBVA2016016000011.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV029894786</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0952-1976(15)00211-0</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">004</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">540</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Haixiang, Guo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">18</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This paper proposes an ensemble algorithm named of BPSO-Adaboost-KNN to cope with multi-class imbalanced data classification. The main idea of this algorithm is to integrate feature selection and boosting into ensemble. What’s more, we utilize a novel evaluation metric called AUCarea which is especially for multi-class classification. In our model BPSO is employed as the feature selection algorithm in which AUCarea is chosen as the fitness. For classification, we generate a boosting classifier in which KNN is selected as the basic classifier. In order to verify the effectiveness of our method, 19 benchmarks are used in our experiments. The results show that the proposed algorithm improves both the stability and the accuracy of boosting after carrying out feature selection, and the performance of our algorithm is comparable with other state-of-the-art algorithms. In statistical analyses, we apply Bland–Altman analysis to show the consistencies between AUCarea and other popular metrics like average G-mean, average F-value etc. Besides, we use linear regression to find deeper correlation between AUCarea and other metrics in order to show why AUCarea works well in this issue. We also put out a series of statistical studies in order to analyze if there exist significant improvements after feature selection and boosting are employed. At last, the proposed algorithm is applied in oil-bearing of reservoir recognition. The classification precision is up to 99% in oilsk81-oilsk85 well logging data in Jianghan oilfield of China, which is 20% higher than KNN classifier. Particularly, the proposed algorithm has significant superiority when distinguishing the oil layer from other layers.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This paper proposes an ensemble algorithm named of BPSO-Adaboost-KNN to cope with multi-class imbalanced data classification. The main idea of this algorithm is to integrate feature selection and boosting into ensemble. What’s more, we utilize a novel evaluation metric called AUCarea which is especially for multi-class classification. In our model BPSO is employed as the feature selection algorithm in which AUCarea is chosen as the fitness. For classification, we generate a boosting classifier in which KNN is selected as the basic classifier. In order to verify the effectiveness of our method, 19 benchmarks are used in our experiments. The results show that the proposed algorithm improves both the stability and the accuracy of boosting after carrying out feature selection, and the performance of our algorithm is comparable with other state-of-the-art algorithms. In statistical analyses, we apply Bland–Altman analysis to show the consistencies between AUCarea and other popular metrics like average G-mean, average F-value etc. Besides, we use linear regression to find deeper correlation between AUCarea and other metrics in order to show why AUCarea works well in this issue. We also put out a series of statistical studies in order to analyze if there exist significant improvements after feature selection and boosting are employed. At last, the proposed algorithm is applied in oil-bearing of reservoir recognition. The classification precision is up to 99% in oilsk81-oilsk85 well logging data in Jianghan oilfield of China, which is 20% higher than KNN classifier. Particularly, the proposed algorithm has significant superiority when distinguishing the oil layer from other layers.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Imbalanced data</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Feature selection</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Ensemble</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Classification</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Oil reservoir</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yijing, Li</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yanan, Li</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xiao, Liu</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jinling, Li</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Liu, Xiang ELSEVIER</subfield><subfield code="t">Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation</subfield><subfield code="d">2015</subfield><subfield code="d">the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV013402978</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:49</subfield><subfield code="g">year:2016</subfield><subfield code="g">pages:176-193</subfield><subfield code="g">extent:18</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.engappai.2015.09.011</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.00</subfield><subfield code="j">Medizin: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">49</subfield><subfield code="j">2016</subfield><subfield code="h">176-193</subfield><subfield code="g">18</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">004</subfield></datafield></record></collection>
|
| author |
Haixiang, Guo |
| spellingShingle |
Haixiang, Guo ddc 004 ddc 540 ddc 610 bkl 44.00 Elsevier Imbalanced data Elsevier Feature selection Elsevier Ensemble Elsevier Classification Elsevier Oil reservoir BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification |
| authorStr |
Haixiang, Guo |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV013402978 |
| format |
electronic Article |
| dewey-ones |
004 - Data processing & computer science 540 - Chemistry & allied sciences 610 - Medicine & health |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
004 004 DE-600 540 VZ 610 VZ 44.00 bkl BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification Imbalanced data Elsevier Feature selection Elsevier Ensemble Elsevier Classification Elsevier Oil reservoir Elsevier |
| topic |
ddc 004 ddc 540 ddc 610 bkl 44.00 Elsevier Imbalanced data Elsevier Feature selection Elsevier Ensemble Elsevier Classification Elsevier Oil reservoir |
| topic_unstemmed |
ddc 004 ddc 540 ddc 610 bkl 44.00 Elsevier Imbalanced data Elsevier Feature selection Elsevier Ensemble Elsevier Classification Elsevier Oil reservoir |
| topic_browse |
ddc 004 ddc 540 ddc 610 bkl 44.00 Elsevier Imbalanced data Elsevier Feature selection Elsevier Ensemble Elsevier Classification Elsevier Oil reservoir |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
l y ly l y ly l x lx l j lj |
| hierarchy_parent_title |
Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation |
| hierarchy_parent_id |
ELV013402978 |
| dewey-tens |
000 - Computer science, knowledge & systems 540 - Chemistry 610 - Medicine & health |
| hierarchy_top_title |
Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV013402978 |
| title |
BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification |
| ctrlnum |
(DE-627)ELV029894786 (ELSEVIER)S0952-1976(15)00211-0 |
| title_full |
BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification |
| author_sort |
Haixiang, Guo |
| journal |
Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation |
| journalStr |
Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works 500 - Science 600 - Technology |
| recordtype |
marc |
| publishDateSort |
2016 |
| contenttype_str_mv |
zzz |
| container_start_page |
176 |
| author_browse |
Haixiang, Guo |
| container_volume |
49 |
| physical |
18 |
| class |
004 004 DE-600 540 VZ 610 VZ 44.00 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Haixiang, Guo |
| doi_str_mv |
10.1016/j.engappai.2015.09.011 |
| dewey-full |
004 540 610 |
| title_sort |
bpso-adaboost-knn ensemble learning algorithm for multi-class imbalanced data classification |
| title_auth |
BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification |
| abstract |
This paper proposes an ensemble algorithm named of BPSO-Adaboost-KNN to cope with multi-class imbalanced data classification. The main idea of this algorithm is to integrate feature selection and boosting into ensemble. What’s more, we utilize a novel evaluation metric called AUCarea which is especially for multi-class classification. In our model BPSO is employed as the feature selection algorithm in which AUCarea is chosen as the fitness. For classification, we generate a boosting classifier in which KNN is selected as the basic classifier. In order to verify the effectiveness of our method, 19 benchmarks are used in our experiments. The results show that the proposed algorithm improves both the stability and the accuracy of boosting after carrying out feature selection, and the performance of our algorithm is comparable with other state-of-the-art algorithms. In statistical analyses, we apply Bland–Altman analysis to show the consistencies between AUCarea and other popular metrics like average G-mean, average F-value etc. Besides, we use linear regression to find deeper correlation between AUCarea and other metrics in order to show why AUCarea works well in this issue. We also put out a series of statistical studies in order to analyze if there exist significant improvements after feature selection and boosting are employed. At last, the proposed algorithm is applied in oil-bearing of reservoir recognition. The classification precision is up to 99% in oilsk81-oilsk85 well logging data in Jianghan oilfield of China, which is 20% higher than KNN classifier. Particularly, the proposed algorithm has significant superiority when distinguishing the oil layer from other layers. |
| abstractGer |
This paper proposes an ensemble algorithm named of BPSO-Adaboost-KNN to cope with multi-class imbalanced data classification. The main idea of this algorithm is to integrate feature selection and boosting into ensemble. What’s more, we utilize a novel evaluation metric called AUCarea which is especially for multi-class classification. In our model BPSO is employed as the feature selection algorithm in which AUCarea is chosen as the fitness. For classification, we generate a boosting classifier in which KNN is selected as the basic classifier. In order to verify the effectiveness of our method, 19 benchmarks are used in our experiments. The results show that the proposed algorithm improves both the stability and the accuracy of boosting after carrying out feature selection, and the performance of our algorithm is comparable with other state-of-the-art algorithms. In statistical analyses, we apply Bland–Altman analysis to show the consistencies between AUCarea and other popular metrics like average G-mean, average F-value etc. Besides, we use linear regression to find deeper correlation between AUCarea and other metrics in order to show why AUCarea works well in this issue. We also put out a series of statistical studies in order to analyze if there exist significant improvements after feature selection and boosting are employed. At last, the proposed algorithm is applied in oil-bearing of reservoir recognition. The classification precision is up to 99% in oilsk81-oilsk85 well logging data in Jianghan oilfield of China, which is 20% higher than KNN classifier. Particularly, the proposed algorithm has significant superiority when distinguishing the oil layer from other layers. |
| abstract_unstemmed |
This paper proposes an ensemble algorithm named of BPSO-Adaboost-KNN to cope with multi-class imbalanced data classification. The main idea of this algorithm is to integrate feature selection and boosting into ensemble. What’s more, we utilize a novel evaluation metric called AUCarea which is especially for multi-class classification. In our model BPSO is employed as the feature selection algorithm in which AUCarea is chosen as the fitness. For classification, we generate a boosting classifier in which KNN is selected as the basic classifier. In order to verify the effectiveness of our method, 19 benchmarks are used in our experiments. The results show that the proposed algorithm improves both the stability and the accuracy of boosting after carrying out feature selection, and the performance of our algorithm is comparable with other state-of-the-art algorithms. In statistical analyses, we apply Bland–Altman analysis to show the consistencies between AUCarea and other popular metrics like average G-mean, average F-value etc. Besides, we use linear regression to find deeper correlation between AUCarea and other metrics in order to show why AUCarea works well in this issue. We also put out a series of statistical studies in order to analyze if there exist significant improvements after feature selection and boosting are employed. At last, the proposed algorithm is applied in oil-bearing of reservoir recognition. The classification precision is up to 99% in oilsk81-oilsk85 well logging data in Jianghan oilfield of China, which is 20% higher than KNN classifier. Particularly, the proposed algorithm has significant superiority when distinguishing the oil layer from other layers. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA |
| title_short |
BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification |
| url |
https://doi.org/10.1016/j.engappai.2015.09.011 |
| remote_bool |
true |
| author2 |
Yijing, Li Yanan, Li Xiao, Liu Jinling, Li |
| author2Str |
Yijing, Li Yanan, Li Xiao, Liu Jinling, Li |
| ppnlink |
ELV013402978 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth oth |
| doi_str |
10.1016/j.engappai.2015.09.011 |
| up_date |
2024-07-06T22:39:13.713Z |
| _version_ |
1803871116292259840 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV029894786</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230625175812.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180603s2016 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.engappai.2015.09.011</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBVA2016016000011.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV029894786</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0952-1976(15)00211-0</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">004</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">540</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Haixiang, Guo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">18</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This paper proposes an ensemble algorithm named of BPSO-Adaboost-KNN to cope with multi-class imbalanced data classification. The main idea of this algorithm is to integrate feature selection and boosting into ensemble. What’s more, we utilize a novel evaluation metric called AUCarea which is especially for multi-class classification. In our model BPSO is employed as the feature selection algorithm in which AUCarea is chosen as the fitness. For classification, we generate a boosting classifier in which KNN is selected as the basic classifier. In order to verify the effectiveness of our method, 19 benchmarks are used in our experiments. The results show that the proposed algorithm improves both the stability and the accuracy of boosting after carrying out feature selection, and the performance of our algorithm is comparable with other state-of-the-art algorithms. In statistical analyses, we apply Bland–Altman analysis to show the consistencies between AUCarea and other popular metrics like average G-mean, average F-value etc. Besides, we use linear regression to find deeper correlation between AUCarea and other metrics in order to show why AUCarea works well in this issue. We also put out a series of statistical studies in order to analyze if there exist significant improvements after feature selection and boosting are employed. At last, the proposed algorithm is applied in oil-bearing of reservoir recognition. The classification precision is up to 99% in oilsk81-oilsk85 well logging data in Jianghan oilfield of China, which is 20% higher than KNN classifier. Particularly, the proposed algorithm has significant superiority when distinguishing the oil layer from other layers.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This paper proposes an ensemble algorithm named of BPSO-Adaboost-KNN to cope with multi-class imbalanced data classification. The main idea of this algorithm is to integrate feature selection and boosting into ensemble. What’s more, we utilize a novel evaluation metric called AUCarea which is especially for multi-class classification. In our model BPSO is employed as the feature selection algorithm in which AUCarea is chosen as the fitness. For classification, we generate a boosting classifier in which KNN is selected as the basic classifier. In order to verify the effectiveness of our method, 19 benchmarks are used in our experiments. The results show that the proposed algorithm improves both the stability and the accuracy of boosting after carrying out feature selection, and the performance of our algorithm is comparable with other state-of-the-art algorithms. In statistical analyses, we apply Bland–Altman analysis to show the consistencies between AUCarea and other popular metrics like average G-mean, average F-value etc. Besides, we use linear regression to find deeper correlation between AUCarea and other metrics in order to show why AUCarea works well in this issue. We also put out a series of statistical studies in order to analyze if there exist significant improvements after feature selection and boosting are employed. At last, the proposed algorithm is applied in oil-bearing of reservoir recognition. The classification precision is up to 99% in oilsk81-oilsk85 well logging data in Jianghan oilfield of China, which is 20% higher than KNN classifier. Particularly, the proposed algorithm has significant superiority when distinguishing the oil layer from other layers.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Imbalanced data</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Feature selection</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Ensemble</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Classification</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Oil reservoir</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yijing, Li</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yanan, Li</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xiao, Liu</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jinling, Li</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Liu, Xiang ELSEVIER</subfield><subfield code="t">Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation</subfield><subfield code="d">2015</subfield><subfield code="d">the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV013402978</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:49</subfield><subfield code="g">year:2016</subfield><subfield code="g">pages:176-193</subfield><subfield code="g">extent:18</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.engappai.2015.09.011</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.00</subfield><subfield code="j">Medizin: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">49</subfield><subfield code="j">2016</subfield><subfield code="h">176-193</subfield><subfield code="g">18</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">004</subfield></datafield></record></collection>
|
| score |
7.4030848 |