Modes of sequential three-way classifications

Abstract

We present a framework for studying sequential three-way classifications based on a sequence of description spaces and a sequence of evaluation functions. In each stage, a pair of a description space and an evaluation function is used for a three-way classification. A set of objects is classified into three regions. The positive region contains positive instances of a given class, the negative region contains negative instances, and the boundary region contains those objects that cannot be classified as positive or negative instances due to insufficient information. By using finer description spaces and finer evaluations, we may be able to make definite classifications for those objects in the boundary region in multiple steps, which gives a sequential three-way classification. We examine four particular modes of sequential three-way classifications with respect to multiple levels of granularity, probabilistic rough set theory, multiple models of classification, and ensemble classifications.