HUANG Qingkang,SONG Kaitao,LU Jianfeng.Application of the loss balance function to the imbalanced multi-classification problems[J].CAAI Transactions on Intelligent Systems,2019,14(05):953-958.[doi:10.11992/tis.201808004]





Application of the loss balance function to the imbalanced multi-classification problems
黄庆康 宋恺涛 陆建峰
南京理工大学 计算机科学与工程学院, 江苏 南京 210094
HUANG Qingkang SONG Kaitao LU Jianfeng
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
imbalanced learningimbalanced data classificationimbalanced multi-classificationloss balanceclassification algorithm for imbalanced dataimbalanced datasetF1 measureconvolutional neural networksdeep learning
The traditional classification algorithms generally require a balanced distribution of various categories in datasets. However, the traditional classification algorithms often encounter an imbalanced class distribution in real life. The existing data- and classifier-level methods that attempt to solve this problem based on different perspectives exhibit some disadvantages, including the selection of parameters that have to be handled carefully and additional computing power because of repeated sampling. To solve these disadvantages, a method that can adaptively maintain the loss balance of examples in a mini-batch is proposed. This algorithm uses a dynamic loss-learnt function to adjust the loss ratio of each sample based on the information present in the label in every mini-batch, thereby achieving a balanced total loss for each class. The experiments conducted using the caltech101 and ILSVRC2014 datasets denote that this algorithm can effectively reduce the computational cost, improve the classification accuracy, and avoid the overfitting risk of the model that can be attributed to the oversampling method.


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