TY - JOUR
A2 - Hua, Kun
AU - Liu, Zhenbing
AU - Gao, Chunyang
AU - Yang, Huihua
AU - He, Qijia
PY - 2016
DA - 2016/12/22
TI - A Cost-Sensitive Sparse Representation Based Classification for Class-Imbalance Problem
SP - 8035089
VL - 2016
AB - 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 F-measure, G-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.
SN - 1058-9244
UR - https://doi.org/10.1155/2016/8035089
DO - 10.1155/2016/8035089
JF - Scientific Programming
PB - Hindawi Publishing Corporation
KW -
ER -