Classification of Imbalanced Data Using Deep Learning with Adding Noise

Wan Wei Fan, Ching Hung Lee*

*此作品的通信作者

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

This paper proposes a method to treat the classification of imbalanced data by adding noise to the feature space of convolutional neural network (CNN) without changing a data set (ratio of majority and minority data). Besides, a hybrid loss function of crossentropy and KL divergence is proposed. The proposed approach can improve the accuracy of minority class in the testing data. In addition, a simple design method for selecting structure of CNN is first introduced and then, we add noise in feature space of CNN to obtain proper features by a training process and to improve the classification results. From comparison results, we can find that the proposed method can extract the suitable features to improve the accuracy of minority class. Finally, illustrated examples of multiclass classification problems and the corresponding discussion in balance ratio are presented. Our approach performs well with smaller network structure compared with other deep models. In addition, the performance is improved over 40% in defective accuracy by adding noise approach. Finally, the accuracy is higher than 96%; even the imbalanced ratio (IR) is one hundred.

原文English
文章編號1735386
期刊Journal of Sensors
2021
DOIs
出版狀態Published - 2021

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