KDV classifier: a novel approach for binary classification
Abstract The current era is an era of Artificial Intelligence. Artificial intelligence is an umbrella discipline that includes Machine Learning as a crucial component. In the Machine Learning space, Classification is an important research area that cannot be neglected. We can define classification a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sharma, Krishna Gopal [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 81(2022), 29 vom: 14. Juli, Seite 42241-42259 |
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| Übergeordnetes Werk: |
volume:81 ; year:2022 ; number:29 ; day:14 ; month:07 ; pages:42241-42259 |
| Links: |
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| DOI / URN: |
10.1007/s11042-021-11451-5 |
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| 520 | |a Abstract The current era is an era of Artificial Intelligence. Artificial intelligence is an umbrella discipline that includes Machine Learning as a crucial component. In the Machine Learning space, Classification is an important research area that cannot be neglected. We can define classification as systematically arranging objects or elements in different groups based on given conditions or criteria. An important class of classifier is a Binary classifier that classifies observations or data into two classes. The binary classifier is useful when observation can only be grouped in two categories or where classification in two classes is required in a given situation. One example of a binary classifier is whether a patient is cancerous or not. In literature many binary classification algorithms are available. The proposed classifier in this research paper is also a binary classifier. The name of the proposed classifier is KDV Binary Classifier. Here KDV stands for K-Distance Variance. K-Distance is the distance of the kth nearest object of a given data point. This binary classifier is particularly useful if observations are not balanced. One particular class outnumbers another class. We compared KDV with KNN for binary classification based on the percentage of accuracy. KNN is a general classifier. We considered its binary aspect. The result shows that KDV is comparable with KNN. Many times KDV outperforms KNN. We compared results for accuracy using cross-validation methods like twofold, fivefold, tenfold and the Also Leave one out method. KDV can be a good research area in the field of Machine Learning. | ||
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10.1007/s11042-021-11451-5 doi (DE-627)OLC2080057847 (DE-He213)s11042-021-11451-5-p DE-627 ger DE-627 rakwb eng 070 004 VZ Sharma, Krishna Gopal verfasserin aut KDV classifier: a novel approach for binary classification 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The current era is an era of Artificial Intelligence. Artificial intelligence is an umbrella discipline that includes Machine Learning as a crucial component. In the Machine Learning space, Classification is an important research area that cannot be neglected. We can define classification as systematically arranging objects or elements in different groups based on given conditions or criteria. An important class of classifier is a Binary classifier that classifies observations or data into two classes. The binary classifier is useful when observation can only be grouped in two categories or where classification in two classes is required in a given situation. One example of a binary classifier is whether a patient is cancerous or not. In literature many binary classification algorithms are available. The proposed classifier in this research paper is also a binary classifier. The name of the proposed classifier is KDV Binary Classifier. Here KDV stands for K-Distance Variance. K-Distance is the distance of the kth nearest object of a given data point. This binary classifier is particularly useful if observations are not balanced. One particular class outnumbers another class. We compared KDV with KNN for binary classification based on the percentage of accuracy. KNN is a general classifier. We considered its binary aspect. The result shows that KDV is comparable with KNN. Many times KDV outperforms KNN. We compared results for accuracy using cross-validation methods like twofold, fivefold, tenfold and the Also Leave one out method. KDV can be a good research area in the field of Machine Learning. Binary classification k-distance k-nearest neighbour KNN KDV Variance Singh, Yashpal aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 29 vom: 14. Juli, Seite 42241-42259 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:29 day:14 month:07 pages:42241-42259 https://doi.org/10.1007/s11042-021-11451-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 29 14 07 42241-42259 |
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10.1007/s11042-021-11451-5 doi (DE-627)OLC2080057847 (DE-He213)s11042-021-11451-5-p DE-627 ger DE-627 rakwb eng 070 004 VZ Sharma, Krishna Gopal verfasserin aut KDV classifier: a novel approach for binary classification 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The current era is an era of Artificial Intelligence. Artificial intelligence is an umbrella discipline that includes Machine Learning as a crucial component. In the Machine Learning space, Classification is an important research area that cannot be neglected. We can define classification as systematically arranging objects or elements in different groups based on given conditions or criteria. An important class of classifier is a Binary classifier that classifies observations or data into two classes. The binary classifier is useful when observation can only be grouped in two categories or where classification in two classes is required in a given situation. One example of a binary classifier is whether a patient is cancerous or not. In literature many binary classification algorithms are available. The proposed classifier in this research paper is also a binary classifier. The name of the proposed classifier is KDV Binary Classifier. Here KDV stands for K-Distance Variance. K-Distance is the distance of the kth nearest object of a given data point. This binary classifier is particularly useful if observations are not balanced. One particular class outnumbers another class. We compared KDV with KNN for binary classification based on the percentage of accuracy. KNN is a general classifier. We considered its binary aspect. The result shows that KDV is comparable with KNN. Many times KDV outperforms KNN. We compared results for accuracy using cross-validation methods like twofold, fivefold, tenfold and the Also Leave one out method. KDV can be a good research area in the field of Machine Learning. Binary classification k-distance k-nearest neighbour KNN KDV Variance Singh, Yashpal aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 29 vom: 14. Juli, Seite 42241-42259 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:29 day:14 month:07 pages:42241-42259 https://doi.org/10.1007/s11042-021-11451-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 29 14 07 42241-42259 |
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10.1007/s11042-021-11451-5 doi (DE-627)OLC2080057847 (DE-He213)s11042-021-11451-5-p DE-627 ger DE-627 rakwb eng 070 004 VZ Sharma, Krishna Gopal verfasserin aut KDV classifier: a novel approach for binary classification 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The current era is an era of Artificial Intelligence. Artificial intelligence is an umbrella discipline that includes Machine Learning as a crucial component. In the Machine Learning space, Classification is an important research area that cannot be neglected. We can define classification as systematically arranging objects or elements in different groups based on given conditions or criteria. An important class of classifier is a Binary classifier that classifies observations or data into two classes. The binary classifier is useful when observation can only be grouped in two categories or where classification in two classes is required in a given situation. One example of a binary classifier is whether a patient is cancerous or not. In literature many binary classification algorithms are available. The proposed classifier in this research paper is also a binary classifier. The name of the proposed classifier is KDV Binary Classifier. Here KDV stands for K-Distance Variance. K-Distance is the distance of the kth nearest object of a given data point. This binary classifier is particularly useful if observations are not balanced. One particular class outnumbers another class. We compared KDV with KNN for binary classification based on the percentage of accuracy. KNN is a general classifier. We considered its binary aspect. The result shows that KDV is comparable with KNN. Many times KDV outperforms KNN. We compared results for accuracy using cross-validation methods like twofold, fivefold, tenfold and the Also Leave one out method. KDV can be a good research area in the field of Machine Learning. Binary classification k-distance k-nearest neighbour KNN KDV Variance Singh, Yashpal aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 29 vom: 14. Juli, Seite 42241-42259 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:29 day:14 month:07 pages:42241-42259 https://doi.org/10.1007/s11042-021-11451-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 29 14 07 42241-42259 |
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10.1007/s11042-021-11451-5 doi (DE-627)OLC2080057847 (DE-He213)s11042-021-11451-5-p DE-627 ger DE-627 rakwb eng 070 004 VZ Sharma, Krishna Gopal verfasserin aut KDV classifier: a novel approach for binary classification 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The current era is an era of Artificial Intelligence. Artificial intelligence is an umbrella discipline that includes Machine Learning as a crucial component. In the Machine Learning space, Classification is an important research area that cannot be neglected. We can define classification as systematically arranging objects or elements in different groups based on given conditions or criteria. An important class of classifier is a Binary classifier that classifies observations or data into two classes. The binary classifier is useful when observation can only be grouped in two categories or where classification in two classes is required in a given situation. One example of a binary classifier is whether a patient is cancerous or not. In literature many binary classification algorithms are available. The proposed classifier in this research paper is also a binary classifier. The name of the proposed classifier is KDV Binary Classifier. Here KDV stands for K-Distance Variance. K-Distance is the distance of the kth nearest object of a given data point. This binary classifier is particularly useful if observations are not balanced. One particular class outnumbers another class. We compared KDV with KNN for binary classification based on the percentage of accuracy. KNN is a general classifier. We considered its binary aspect. The result shows that KDV is comparable with KNN. Many times KDV outperforms KNN. We compared results for accuracy using cross-validation methods like twofold, fivefold, tenfold and the Also Leave one out method. KDV can be a good research area in the field of Machine Learning. Binary classification k-distance k-nearest neighbour KNN KDV Variance Singh, Yashpal aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 29 vom: 14. Juli, Seite 42241-42259 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:29 day:14 month:07 pages:42241-42259 https://doi.org/10.1007/s11042-021-11451-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 29 14 07 42241-42259 |
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KDV classifier: a novel approach for binary classification |
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Abstract The current era is an era of Artificial Intelligence. Artificial intelligence is an umbrella discipline that includes Machine Learning as a crucial component. In the Machine Learning space, Classification is an important research area that cannot be neglected. We can define classification as systematically arranging objects or elements in different groups based on given conditions or criteria. An important class of classifier is a Binary classifier that classifies observations or data into two classes. The binary classifier is useful when observation can only be grouped in two categories or where classification in two classes is required in a given situation. One example of a binary classifier is whether a patient is cancerous or not. In literature many binary classification algorithms are available. The proposed classifier in this research paper is also a binary classifier. The name of the proposed classifier is KDV Binary Classifier. Here KDV stands for K-Distance Variance. K-Distance is the distance of the kth nearest object of a given data point. This binary classifier is particularly useful if observations are not balanced. One particular class outnumbers another class. We compared KDV with KNN for binary classification based on the percentage of accuracy. KNN is a general classifier. We considered its binary aspect. The result shows that KDV is comparable with KNN. Many times KDV outperforms KNN. We compared results for accuracy using cross-validation methods like twofold, fivefold, tenfold and the Also Leave one out method. KDV can be a good research area in the field of Machine Learning. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstractGer |
Abstract The current era is an era of Artificial Intelligence. Artificial intelligence is an umbrella discipline that includes Machine Learning as a crucial component. In the Machine Learning space, Classification is an important research area that cannot be neglected. We can define classification as systematically arranging objects or elements in different groups based on given conditions or criteria. An important class of classifier is a Binary classifier that classifies observations or data into two classes. The binary classifier is useful when observation can only be grouped in two categories or where classification in two classes is required in a given situation. One example of a binary classifier is whether a patient is cancerous or not. In literature many binary classification algorithms are available. The proposed classifier in this research paper is also a binary classifier. The name of the proposed classifier is KDV Binary Classifier. Here KDV stands for K-Distance Variance. K-Distance is the distance of the kth nearest object of a given data point. This binary classifier is particularly useful if observations are not balanced. One particular class outnumbers another class. We compared KDV with KNN for binary classification based on the percentage of accuracy. KNN is a general classifier. We considered its binary aspect. The result shows that KDV is comparable with KNN. Many times KDV outperforms KNN. We compared results for accuracy using cross-validation methods like twofold, fivefold, tenfold and the Also Leave one out method. KDV can be a good research area in the field of Machine Learning. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract The current era is an era of Artificial Intelligence. Artificial intelligence is an umbrella discipline that includes Machine Learning as a crucial component. In the Machine Learning space, Classification is an important research area that cannot be neglected. We can define classification as systematically arranging objects or elements in different groups based on given conditions or criteria. An important class of classifier is a Binary classifier that classifies observations or data into two classes. The binary classifier is useful when observation can only be grouped in two categories or where classification in two classes is required in a given situation. One example of a binary classifier is whether a patient is cancerous or not. In literature many binary classification algorithms are available. The proposed classifier in this research paper is also a binary classifier. The name of the proposed classifier is KDV Binary Classifier. Here KDV stands for K-Distance Variance. K-Distance is the distance of the kth nearest object of a given data point. This binary classifier is particularly useful if observations are not balanced. One particular class outnumbers another class. We compared KDV with KNN for binary classification based on the percentage of accuracy. KNN is a general classifier. We considered its binary aspect. The result shows that KDV is comparable with KNN. Many times KDV outperforms KNN. We compared results for accuracy using cross-validation methods like twofold, fivefold, tenfold and the Also Leave one out method. KDV can be a good research area in the field of Machine Learning. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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| title_short |
KDV classifier: a novel approach for binary classification |
| url |
https://doi.org/10.1007/s11042-021-11451-5 |
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| author2 |
Singh, Yashpal |
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Singh, Yashpal |
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10.1007/s11042-021-11451-5 |
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2024-07-04T02:48:36.308Z |
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