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Cost-sensitive sparse representation based classification

机译：基于成本敏感的稀疏表示的分类

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摘要

Sparse representation has been successfully used in pattern recognition and machine learning. However, most existing sparse representation based classification (SRC) methods are to achieve the highest classification accuracy, assuming the same losses for different misclassifications. This assumption, however, may not hold in many practical applications as different types of misclassification could lead to different losses. To address this problem, we propose a novel cost-sensitive sparse representation based classification (CSSRC) method by using probabilistic modeling. Unlike traditional methods, we predict the class label of test samples by minimizing the misclassification losses, which are obtained via computing the posterior probabilities. Experimental results on the UCI databases validate the efficacy of the proposed approach on average misclassification cost, positive class misclassification rate and negative class misclassification rate. In addition, the experiments show that our proposed method performs competitively compared to SRC, CSSVM and CS4VM.
机译：稀疏表示已成功用于模式识别和机器学习。但是，大多数现有的基于稀疏表示的分类（SRC）方法都将实现最高的分类精度，并假设由于不同的错误分类而造成的损失相同。但是，这种假设在许多实际应用中可能不成立，因为不同类型的错误分类可能导致不同的损失。为了解决这个问题，我们提出了一种新的基于概率敏感的基于成本敏感的基于稀疏表示的分类（CSSRC）方法。与传统方法不同，我们通过最小化通过计算后验概率获得的误分类损失来预测测试样品的分类标签。在UCI数据库上进行的实验结果验证了该方法在平均误分类成本，正类误分类率和负类误分类率方面的有效性。此外，实验表明，与SRC，CSSVM和CS4VM相比，我们提出的方法具有竞争优势。

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