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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2019

10.01.2019

Learning from Imbalanced Data Sets with Weighted Cross-Entropy Function

verfasst von: Yuri Sousa Aurelio, Gustavo Matheus de Almeida, Cristiano Leite de Castro, Antonio Padua Braga

Erschienen in: Neural Processing Letters | Ausgabe 2/2019

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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.
Vorheriger Artikel Recent Deep Learning Techniques, Challenges and Its Applications for Medical Healthcare System: A Review
Nächster Artikel A Fast Algorithm for Convolutional Neural Networks Using Tile-based Fast Fourier Transforms

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Metadaten
Titel
Learning from Imbalanced Data Sets with Weighted Cross-Entropy Function
verfasst von
Yuri Sousa Aurelio
Gustavo Matheus de Almeida
Cristiano Leite de Castro
Antonio Padua Braga
Publikationsdatum
10.01.2019
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2019
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-018-09977-1

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