The classification of imbalanced large data sets based on MapReduce and ensemble of ELM classifiers
Abstract Aiming at effectively classifying imbalanced large data sets with two classes, this paper proposed a novel algorithm, which consists of four stages: (1) alternately over-sample p times between positive class instances and negative class instances; (2) construct l balanced data subsets based...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhai, Junhai [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag Berlin Heidelberg 2015 |
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| Übergeordnetes Werk: |
Enthalten in: International journal of machine learning and cybernetics - Heidelberg : Springer, 2010, 8(2015), 3 vom: 23. Dez., Seite 1009-1017 |
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| Übergeordnetes Werk: |
volume:8 ; year:2015 ; number:3 ; day:23 ; month:12 ; pages:1009-1017 |
| Links: |
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| DOI / URN: |
10.1007/s13042-015-0478-7 |
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| Katalog-ID: |
SPR029602920 |
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| 100 | 1 | |a Zhai, Junhai |e verfasserin |4 aut | |
| 245 | 1 | 4 | |a The classification of imbalanced large data sets based on MapReduce and ensemble of ELM classifiers |
| 264 | 1 | |c 2015 | |
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| 520 | |a Abstract Aiming at effectively classifying imbalanced large data sets with two classes, this paper proposed a novel algorithm, which consists of four stages: (1) alternately over-sample p times between positive class instances and negative class instances; (2) construct l balanced data subsets based on the generated positive class instances; (3) train l component classifiers with extreme learning machine (ELM) algorithm on the constructed l balanced data subsets; (4) integrate the l ELM classifiers with simple voting approach. Specifically, in first stage, we firstly calculate the center of positive class instances, and then sample instance points along the line between the center and each positive class instance. Next, for each instance point in the new positive class, we firstly find its k nearest neighbors in negative class instances with MapRedcue, and then sample instance points along the line between the instance and its k nearest negative neighbors. The process of over-sampling is repeated p times. In the second stage, we sample instances l times from the negative class with the same size as the generated positive class instances. Each round of sampling, we put positive class and negative class instances together thus obtain l balanced data subsets. The experimental results show that the proposed algorithm can obtain promising speed-up and scalability, and also outperforms three other ensemble algorithms in G-mean. | ||
| 650 | 4 | |a Imbalanced large data sets |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a MapReduce |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Extreme learning machine |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Ensemble learning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Majority voting method |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Zhang, Sufang |4 aut | |
| 700 | 1 | |a Wang, Chenxi |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t International journal of machine learning and cybernetics |d Heidelberg : Springer, 2010 |g 8(2015), 3 vom: 23. Dez., Seite 1009-1017 |w (DE-627)635135132 |w (DE-600)2572473-3 |x 1868-808X |7 nnns |
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10.1007/s13042-015-0478-7 doi (DE-627)SPR029602920 (SPR)s13042-015-0478-7-e DE-627 ger DE-627 rakwb eng Zhai, Junhai verfasserin aut The classification of imbalanced large data sets based on MapReduce and ensemble of ELM classifiers 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2015 Abstract Aiming at effectively classifying imbalanced large data sets with two classes, this paper proposed a novel algorithm, which consists of four stages: (1) alternately over-sample p times between positive class instances and negative class instances; (2) construct l balanced data subsets based on the generated positive class instances; (3) train l component classifiers with extreme learning machine (ELM) algorithm on the constructed l balanced data subsets; (4) integrate the l ELM classifiers with simple voting approach. Specifically, in first stage, we firstly calculate the center of positive class instances, and then sample instance points along the line between the center and each positive class instance. Next, for each instance point in the new positive class, we firstly find its k nearest neighbors in negative class instances with MapRedcue, and then sample instance points along the line between the instance and its k nearest negative neighbors. The process of over-sampling is repeated p times. In the second stage, we sample instances l times from the negative class with the same size as the generated positive class instances. Each round of sampling, we put positive class and negative class instances together thus obtain l balanced data subsets. The experimental results show that the proposed algorithm can obtain promising speed-up and scalability, and also outperforms three other ensemble algorithms in G-mean. Imbalanced large data sets (dpeaa)DE-He213 MapReduce (dpeaa)DE-He213 Extreme learning machine (dpeaa)DE-He213 Ensemble learning (dpeaa)DE-He213 Majority voting method (dpeaa)DE-He213 Zhang, Sufang aut Wang, Chenxi aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 8(2015), 3 vom: 23. Dez., Seite 1009-1017 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:8 year:2015 number:3 day:23 month:12 pages:1009-1017 https://dx.doi.org/10.1007/s13042-015-0478-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2015 3 23 12 1009-1017 |
| spelling |
10.1007/s13042-015-0478-7 doi (DE-627)SPR029602920 (SPR)s13042-015-0478-7-e DE-627 ger DE-627 rakwb eng Zhai, Junhai verfasserin aut The classification of imbalanced large data sets based on MapReduce and ensemble of ELM classifiers 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2015 Abstract Aiming at effectively classifying imbalanced large data sets with two classes, this paper proposed a novel algorithm, which consists of four stages: (1) alternately over-sample p times between positive class instances and negative class instances; (2) construct l balanced data subsets based on the generated positive class instances; (3) train l component classifiers with extreme learning machine (ELM) algorithm on the constructed l balanced data subsets; (4) integrate the l ELM classifiers with simple voting approach. Specifically, in first stage, we firstly calculate the center of positive class instances, and then sample instance points along the line between the center and each positive class instance. Next, for each instance point in the new positive class, we firstly find its k nearest neighbors in negative class instances with MapRedcue, and then sample instance points along the line between the instance and its k nearest negative neighbors. The process of over-sampling is repeated p times. In the second stage, we sample instances l times from the negative class with the same size as the generated positive class instances. Each round of sampling, we put positive class and negative class instances together thus obtain l balanced data subsets. The experimental results show that the proposed algorithm can obtain promising speed-up and scalability, and also outperforms three other ensemble algorithms in G-mean. Imbalanced large data sets (dpeaa)DE-He213 MapReduce (dpeaa)DE-He213 Extreme learning machine (dpeaa)DE-He213 Ensemble learning (dpeaa)DE-He213 Majority voting method (dpeaa)DE-He213 Zhang, Sufang aut Wang, Chenxi aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 8(2015), 3 vom: 23. Dez., Seite 1009-1017 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:8 year:2015 number:3 day:23 month:12 pages:1009-1017 https://dx.doi.org/10.1007/s13042-015-0478-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2015 3 23 12 1009-1017 |
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10.1007/s13042-015-0478-7 doi (DE-627)SPR029602920 (SPR)s13042-015-0478-7-e DE-627 ger DE-627 rakwb eng Zhai, Junhai verfasserin aut The classification of imbalanced large data sets based on MapReduce and ensemble of ELM classifiers 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2015 Abstract Aiming at effectively classifying imbalanced large data sets with two classes, this paper proposed a novel algorithm, which consists of four stages: (1) alternately over-sample p times between positive class instances and negative class instances; (2) construct l balanced data subsets based on the generated positive class instances; (3) train l component classifiers with extreme learning machine (ELM) algorithm on the constructed l balanced data subsets; (4) integrate the l ELM classifiers with simple voting approach. Specifically, in first stage, we firstly calculate the center of positive class instances, and then sample instance points along the line between the center and each positive class instance. Next, for each instance point in the new positive class, we firstly find its k nearest neighbors in negative class instances with MapRedcue, and then sample instance points along the line between the instance and its k nearest negative neighbors. The process of over-sampling is repeated p times. In the second stage, we sample instances l times from the negative class with the same size as the generated positive class instances. Each round of sampling, we put positive class and negative class instances together thus obtain l balanced data subsets. The experimental results show that the proposed algorithm can obtain promising speed-up and scalability, and also outperforms three other ensemble algorithms in G-mean. Imbalanced large data sets (dpeaa)DE-He213 MapReduce (dpeaa)DE-He213 Extreme learning machine (dpeaa)DE-He213 Ensemble learning (dpeaa)DE-He213 Majority voting method (dpeaa)DE-He213 Zhang, Sufang aut Wang, Chenxi aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 8(2015), 3 vom: 23. Dez., Seite 1009-1017 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:8 year:2015 number:3 day:23 month:12 pages:1009-1017 https://dx.doi.org/10.1007/s13042-015-0478-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2015 3 23 12 1009-1017 |
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10.1007/s13042-015-0478-7 doi (DE-627)SPR029602920 (SPR)s13042-015-0478-7-e DE-627 ger DE-627 rakwb eng Zhai, Junhai verfasserin aut The classification of imbalanced large data sets based on MapReduce and ensemble of ELM classifiers 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2015 Abstract Aiming at effectively classifying imbalanced large data sets with two classes, this paper proposed a novel algorithm, which consists of four stages: (1) alternately over-sample p times between positive class instances and negative class instances; (2) construct l balanced data subsets based on the generated positive class instances; (3) train l component classifiers with extreme learning machine (ELM) algorithm on the constructed l balanced data subsets; (4) integrate the l ELM classifiers with simple voting approach. Specifically, in first stage, we firstly calculate the center of positive class instances, and then sample instance points along the line between the center and each positive class instance. Next, for each instance point in the new positive class, we firstly find its k nearest neighbors in negative class instances with MapRedcue, and then sample instance points along the line between the instance and its k nearest negative neighbors. The process of over-sampling is repeated p times. In the second stage, we sample instances l times from the negative class with the same size as the generated positive class instances. Each round of sampling, we put positive class and negative class instances together thus obtain l balanced data subsets. The experimental results show that the proposed algorithm can obtain promising speed-up and scalability, and also outperforms three other ensemble algorithms in G-mean. Imbalanced large data sets (dpeaa)DE-He213 MapReduce (dpeaa)DE-He213 Extreme learning machine (dpeaa)DE-He213 Ensemble learning (dpeaa)DE-He213 Majority voting method (dpeaa)DE-He213 Zhang, Sufang aut Wang, Chenxi aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 8(2015), 3 vom: 23. Dez., Seite 1009-1017 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:8 year:2015 number:3 day:23 month:12 pages:1009-1017 https://dx.doi.org/10.1007/s13042-015-0478-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2015 3 23 12 1009-1017 |
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10.1007/s13042-015-0478-7 doi (DE-627)SPR029602920 (SPR)s13042-015-0478-7-e DE-627 ger DE-627 rakwb eng Zhai, Junhai verfasserin aut The classification of imbalanced large data sets based on MapReduce and ensemble of ELM classifiers 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2015 Abstract Aiming at effectively classifying imbalanced large data sets with two classes, this paper proposed a novel algorithm, which consists of four stages: (1) alternately over-sample p times between positive class instances and negative class instances; (2) construct l balanced data subsets based on the generated positive class instances; (3) train l component classifiers with extreme learning machine (ELM) algorithm on the constructed l balanced data subsets; (4) integrate the l ELM classifiers with simple voting approach. Specifically, in first stage, we firstly calculate the center of positive class instances, and then sample instance points along the line between the center and each positive class instance. Next, for each instance point in the new positive class, we firstly find its k nearest neighbors in negative class instances with MapRedcue, and then sample instance points along the line between the instance and its k nearest negative neighbors. The process of over-sampling is repeated p times. In the second stage, we sample instances l times from the negative class with the same size as the generated positive class instances. Each round of sampling, we put positive class and negative class instances together thus obtain l balanced data subsets. The experimental results show that the proposed algorithm can obtain promising speed-up and scalability, and also outperforms three other ensemble algorithms in G-mean. Imbalanced large data sets (dpeaa)DE-He213 MapReduce (dpeaa)DE-He213 Extreme learning machine (dpeaa)DE-He213 Ensemble learning (dpeaa)DE-He213 Majority voting method (dpeaa)DE-He213 Zhang, Sufang aut Wang, Chenxi aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 8(2015), 3 vom: 23. Dez., Seite 1009-1017 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:8 year:2015 number:3 day:23 month:12 pages:1009-1017 https://dx.doi.org/10.1007/s13042-015-0478-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2015 3 23 12 1009-1017 |
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International journal of machine learning and cybernetics |
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Zhai, Junhai @@aut@@ Zhang, Sufang @@aut@@ Wang, Chenxi @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR029602920</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230331104706.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2015 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s13042-015-0478-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR029602920</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s13042-015-0478-7-e</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="100" ind1="1" ind2=" "><subfield code="a">Zhai, Junhai</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="4"><subfield code="a">The classification of imbalanced large data sets based on MapReduce and ensemble of ELM classifiers</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag Berlin Heidelberg 2015</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Aiming at effectively classifying imbalanced large data sets with two classes, this paper proposed a novel algorithm, which consists of four stages: (1) alternately over-sample p times between positive class instances and negative class instances; (2) construct l balanced data subsets based on the generated positive class instances; (3) train l component classifiers with extreme learning machine (ELM) algorithm on the constructed l balanced data subsets; (4) integrate the l ELM classifiers with simple voting approach. Specifically, in first stage, we firstly calculate the center of positive class instances, and then sample instance points along the line between the center and each positive class instance. Next, for each instance point in the new positive class, we firstly find its k nearest neighbors in negative class instances with MapRedcue, and then sample instance points along the line between the instance and its k nearest negative neighbors. The process of over-sampling is repeated p times. In the second stage, we sample instances l times from the negative class with the same size as the generated positive class instances. Each round of sampling, we put positive class and negative class instances together thus obtain l balanced data subsets. The experimental results show that the proposed algorithm can obtain promising speed-up and scalability, and also outperforms three other ensemble algorithms in G-mean.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Imbalanced large data sets</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">MapReduce</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Extreme learning machine</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ensemble learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Majority voting method</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Sufang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Chenxi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of machine learning and cybernetics</subfield><subfield code="d">Heidelberg : Springer, 2010</subfield><subfield code="g">8(2015), 3 vom: 23. 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Zhai, Junhai |
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Zhai, Junhai misc Imbalanced large data sets misc MapReduce misc Extreme learning machine misc Ensemble learning misc Majority voting method The classification of imbalanced large data sets based on MapReduce and ensemble of ELM classifiers |
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The classification of imbalanced large data sets based on MapReduce and ensemble of ELM classifiers Imbalanced large data sets (dpeaa)DE-He213 MapReduce (dpeaa)DE-He213 Extreme learning machine (dpeaa)DE-He213 Ensemble learning (dpeaa)DE-He213 Majority voting method (dpeaa)DE-He213 |
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classification of imbalanced large data sets based on mapreduce and ensemble of elm classifiers |
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The classification of imbalanced large data sets based on MapReduce and ensemble of ELM classifiers |
| abstract |
Abstract Aiming at effectively classifying imbalanced large data sets with two classes, this paper proposed a novel algorithm, which consists of four stages: (1) alternately over-sample p times between positive class instances and negative class instances; (2) construct l balanced data subsets based on the generated positive class instances; (3) train l component classifiers with extreme learning machine (ELM) algorithm on the constructed l balanced data subsets; (4) integrate the l ELM classifiers with simple voting approach. Specifically, in first stage, we firstly calculate the center of positive class instances, and then sample instance points along the line between the center and each positive class instance. Next, for each instance point in the new positive class, we firstly find its k nearest neighbors in negative class instances with MapRedcue, and then sample instance points along the line between the instance and its k nearest negative neighbors. The process of over-sampling is repeated p times. In the second stage, we sample instances l times from the negative class with the same size as the generated positive class instances. Each round of sampling, we put positive class and negative class instances together thus obtain l balanced data subsets. The experimental results show that the proposed algorithm can obtain promising speed-up and scalability, and also outperforms three other ensemble algorithms in G-mean. © Springer-Verlag Berlin Heidelberg 2015 |
| abstractGer |
Abstract Aiming at effectively classifying imbalanced large data sets with two classes, this paper proposed a novel algorithm, which consists of four stages: (1) alternately over-sample p times between positive class instances and negative class instances; (2) construct l balanced data subsets based on the generated positive class instances; (3) train l component classifiers with extreme learning machine (ELM) algorithm on the constructed l balanced data subsets; (4) integrate the l ELM classifiers with simple voting approach. Specifically, in first stage, we firstly calculate the center of positive class instances, and then sample instance points along the line between the center and each positive class instance. Next, for each instance point in the new positive class, we firstly find its k nearest neighbors in negative class instances with MapRedcue, and then sample instance points along the line between the instance and its k nearest negative neighbors. The process of over-sampling is repeated p times. In the second stage, we sample instances l times from the negative class with the same size as the generated positive class instances. Each round of sampling, we put positive class and negative class instances together thus obtain l balanced data subsets. The experimental results show that the proposed algorithm can obtain promising speed-up and scalability, and also outperforms three other ensemble algorithms in G-mean. © Springer-Verlag Berlin Heidelberg 2015 |
| abstract_unstemmed |
Abstract Aiming at effectively classifying imbalanced large data sets with two classes, this paper proposed a novel algorithm, which consists of four stages: (1) alternately over-sample p times between positive class instances and negative class instances; (2) construct l balanced data subsets based on the generated positive class instances; (3) train l component classifiers with extreme learning machine (ELM) algorithm on the constructed l balanced data subsets; (4) integrate the l ELM classifiers with simple voting approach. Specifically, in first stage, we firstly calculate the center of positive class instances, and then sample instance points along the line between the center and each positive class instance. Next, for each instance point in the new positive class, we firstly find its k nearest neighbors in negative class instances with MapRedcue, and then sample instance points along the line between the instance and its k nearest negative neighbors. The process of over-sampling is repeated p times. In the second stage, we sample instances l times from the negative class with the same size as the generated positive class instances. Each round of sampling, we put positive class and negative class instances together thus obtain l balanced data subsets. The experimental results show that the proposed algorithm can obtain promising speed-up and scalability, and also outperforms three other ensemble algorithms in G-mean. © Springer-Verlag Berlin Heidelberg 2015 |
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| title_short |
The classification of imbalanced large data sets based on MapReduce and ensemble of ELM classifiers |
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https://dx.doi.org/10.1007/s13042-015-0478-7 |
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true |
| author2 |
Zhang, Sufang Wang, Chenxi |
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Zhang, Sufang Wang, Chenxi |
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| doi_str |
10.1007/s13042-015-0478-7 |
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2024-07-04T01:39:31.486Z |
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| score |
7.397564 |