Cost-sensitive matrixized classification learning with information entropy

Abstract

Classifier design is one of the most significant fields in pattern recognition. Most classifiers are measured by classification accuracy, which assumes that all the misclassification cost are the same. In the real world, different misclassifications usually bring different losses. Based on this fact, cost-sensitive learning is becoming a hot research area in pattern recognition. However, in cost-sensitive learning, examples costs are often difficult to achieve and usually decided by the authors experience. Hence, combining the cost-sensitive learning and matrixized learning thoughts, we propose a two-class cost-sensitive matrixized classification model based on information entropy called CsMatMHKS in this paper. The proposed CsMatMHKS introduces information entropy which can reveal the uncertainty of one sample into matrixized learning framework to decrease the total misclassification cost. The experimental results on the UCI datasets and image datasets indicate that the CsMatMHKS not only reduces the sum of classification costs but also has comparable classification accuracy.