A multiphase cost-sensitive learning method based on the multiclass three-way decision-theoretic rough set model

摘要

The decision-theoretic rough set (DTRS) can be regarded as a type of cost-sensitive learning method that incorporates cost functions. Three-way decisions for an object are made in the DTRS model to obtain the minimum Bayesian decision cost, including acceptance, rejection and deferment decisions. However, because the cost matrix in traditional cost-sensitive learning problems does not include the cost for deferment decisions, DTRS cannot be directly used to solve traditional cost-sensitive learning problems. In this paper, we present a multiclass three-way decision-theoretic rough set model that can be directly applied to traditional cost-sensitive learning problems. This model can calculate the corresponding cost for deferment decisions based on the traditional cost matrix. In addition, we propose a multiphase cost-sensitive learning method based on the multiclass three-way decision-theoretic rough set model. The deferment and rejection samples are reduced and eliminated through multiphase iterative learning. Compared with the existing multiclass cost-sensitive learning methods, our proposed method can achieve higher classification accuracy and lower misclassification cost. (C) 2019 Elsevier Inc. All rights reserved.