Combating the class imbalance problem in sparse representation learning

Abstract

Recent studies have shown sparse representation learning is a potentially promising method in pattern classification, but very few focused on class imbalanced problems involved in its applications and practice. This problem is particularly important, since it causes suboptimal classification performances, especially when the cost of misclassifying a minority-class example is substantial. Unlike the prior test sample sparse representation on balanced data sets, which cannot reflect the data distribution in real applications, we proposed a novel sparse representation learning algorithm called Balanced Sparse Representation Classifier (BSRC), considering the contribution from heavily under-represented of minority classes. Our solution first estimates the contribution of training sample in each class, and then identifies the nearest neighbors with the largest contributions. After that, the test data is expressed based on linear combination of all the nearest samples. Finally, the decision has been made according to sum of contribution for each class. Moreover, we also present the kernel extension of the proposed classifier to deal with complex data. Experimental results also show that with the proposed learning approach, it is possible to design better method to tackle the class imbalance problem in sparse representation learning.