sparserepresentation has been successfully used in pattern recognition and machine learning. However, most existing sparserepresentationbasedclassification(src) methods are to achieve the highest classification ac...
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ISBN:
(纸本)9781509012572
sparserepresentation has been successfully used in pattern recognition and machine learning. However, most existing sparserepresentationbasedclassification(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 sparserepresentationbasedclassification(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 CS4 VM.
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