Two-phase classification based on three-way decisions

Abstract

A two-phase classification method is proposed based on three-way decisions. In the first phase, all objects are classified into three different regions by three-way decisions. A positive rule makes a decision of acceptance, a negative rule makes a decision of rejection, and a boundary rule makes a decision of abstaining. The positive region contains those objects that have been assigned a class label with a high level of confidence. The boundary and negative regions contain those objects that have not been assigned class labels. In the second phase, a simple ensemble learning approach to determine the class labels of objects in the boundary or negative regions. Experiments are performed to compare the proposed two-phase classification approach and a classical classification approach. The results show that our method can produce a better classification accuracy than the classical model. © 2013 Springer-Verlag.