A decision-theoretic rough set approach to multi-class cost-sensitive classification

Abstract

As a kind of probabilistic rough set model, decision-theoretic rough set is usually used to deal with binary classification problems. This paper provides a new formulation of multi-class decision-theoretic rough set by combining decision-theoretic rough set model with classical cost-sensitive learning. Upper approximation, lower approximation, positive region, negative region and boundary region can be derived from the n × n cost matrix of classical multi-class situation. The probability thresholds for three-way decisions making are defined. A cost-sensitive classification algorithm based on multi-class decision-theoretic rough set model is presented. The experimental results on several UCI data sets indicate that the proposed algorithm can get a better performance on classification accuracy and total cost.