Table of Contents Author Guidelines Submit a Manuscript
Scientific Programming
Volume 2016 (2016), Article ID 8035089, 9 pages
Research Article

A Cost-Sensitive Sparse Representation Based Classification for Class-Imbalance Problem

1School of Computer and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
2School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China
3School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received 8 August 2016; Accepted 16 October 2016

Academic Editor: Kun Hua

Copyright © 2016 Zhenbing Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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. In real-world application, much data sets are imbalanced of the class distribution. To address these problems, we propose a cost-sensitive sparse representation based classification (CSSRC) for class-imbalance problem method by using probabilistic modeling. Unlike traditional SRC 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, we sampled test samples and training samples with different imbalance ratio and use -measure, -mean, classification accuracy, and running time to evaluate the performance of the proposed method. The experiments show that our proposed method performs competitively compared to SRC, CSSVM, and CS4VM.