[1]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(5):953-958.[doi:10.11992/tis.201808004]
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Application of the loss balance function to the imbalanced multi-classification problems
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
14
Number of periods:
2019 5
Page number:
953-958
Column:
学术论文—人工智能基础
Public date:
2019-09-05
- Title:
- Application of the loss balance function to the imbalanced multi-classification problems
- Keywords:
- imbalanced learning; imbalanced data classification; imbalanced multi-classification; loss balance; classification algorithm for imbalanced data; imbalanced dataset; F1 measure; convolutional neural networks; deep learning
- CLC:
- TP391
- DOI:
- 10.11992/tis.201808004
- Abstract:
- 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.
- References:
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Last Update:
1900-01-01