Combating the class imbalance problem in sparse representation learning
Combating the class imbalance problem in sparse representation learning
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解决稀疏表示学习中的类别不平衡问题
DOI：
10.3233/jifs-171342
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发表时间：
2018
影响因子：
2
通讯作者：
Yuming Chen
中科院分区：
文献类型：
--
作者：
Ying Ma;Xiatian Zhu;Shunzhi Zhu;Keshou Wu;Yuming Chen
近期研究表明，稀疏表示学习在模式分类中是一种具有潜在前景的方法，但很少有研究关注其应用和实践中涉及的类别不平衡问题。这个问题尤为重要，因为它会导致分类性能欠佳，特别是当误分类少数类样本的代价很高时。与之前在平衡数据集上对测试样本进行稀疏表示不同（这种方式无法反映实际应用中的数据分布），我们考虑到少数类严重代表性不足的情况，提出了一种新的稀疏表示学习算法，称为平衡稀疏表示分类器（BSRC）。我们的解决方案首先估计每个类别中训练样本的贡献，然后找出贡献最大的最近邻。之后，基于所有最近邻样本的线性组合来表示测试数据。最后，根据每个类别的贡献总和做出决策。此外，我们还提出了所提分类器的核扩展以处理复杂数据。实验结果还表明，利用所提出的学习方法，有可能设计出更好的方法来解决稀疏表示学习中的类别不平衡问题。
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.