Learning from Imbalanced Data Sets with Weighted Cross-Entropy Function
This paper presents a novel approach to deal with the imbalanced data set problem in neural networks by incorporating prior probabilities into a cost-sensitive cross-entropy error function. Several classical benchmarks were tested for performance evaluation using different metrics, namely G-Mean, ar...
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| Published in | Neural processing letters Vol. 50; no. 2; pp. 1937 - 1949 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.10.2019
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1370-4621 1573-773X |
| DOI | 10.1007/s11063-018-09977-1 |
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| Abstract | This paper presents a novel approach to deal with the imbalanced data set problem in neural networks by incorporating prior probabilities into a cost-sensitive cross-entropy error function. Several classical benchmarks were tested for performance evaluation using different metrics, namely G-Mean, area under the ROC curve (AUC), adjusted G-Mean, Accuracy, True Positive Rate, True Negative Rate and F1-score. The obtained results were compared to well-known algorithms and showed the effectiveness and robustness of the proposed approach, which results in well-balanced classifiers given different imbalance scenarios. |
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| AbstractList | This paper presents a novel approach to deal with the imbalanced data set problem in neural networks by incorporating prior probabilities into a cost-sensitive cross-entropy error function. Several classical benchmarks were tested for performance evaluation using different metrics, namely G-Mean, area under the ROC curve (AUC), adjusted G-Mean, Accuracy, True Positive Rate, True Negative Rate and F1-score. The obtained results were compared to well-known algorithms and showed the effectiveness and robustness of the proposed approach, which results in well-balanced classifiers given different imbalance scenarios. |
| Author | Braga, Antonio Padua Aurelio, Yuri Sousa de Castro, Cristiano Leite de Almeida, Gustavo Matheus |
| Author_xml | – sequence: 1 givenname: Yuri Sousa orcidid: 0000-0003-2777-1246 surname: Aurelio fullname: Aurelio, Yuri Sousa email: yurisousa@ufmg.br organization: Federal University of Minas Gerais – sequence: 2 givenname: Gustavo Matheus surname: de Almeida fullname: de Almeida, Gustavo Matheus organization: Federal University of Minas Gerais – sequence: 3 givenname: Cristiano Leite surname: de Castro fullname: de Castro, Cristiano Leite organization: Federal University of Minas Gerais – sequence: 4 givenname: Antonio Padua surname: Braga fullname: Braga, Antonio Padua organization: Federal University of Minas Gerais |
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| Copyright | Springer Science+Business Media, LLC, part of Springer Nature 2019 Copyright Springer Nature B.V. Oct 2019 |
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| Keywords | Classification problem Imbalanced data Multilayer perceptron Back-propagation Cost-sensitive function |
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| Title | Learning from Imbalanced Data Sets with Weighted Cross-Entropy Function |
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