Coarsening Classification Rules on Basis of Granular Computing

Abstract

The task of classification, as a well-studied field of machine learning and data mining, has two main purposes: describing the classification rules in the training dataset and predicting the unseen new instances in the testing dataset. Normally, one expects a high accuracy for precisely descriptive use. However, a higher accuracy is intended to generate longer rules, which are not easy to understand, and may overfit the training instances. This motivates us to propose the approach of coarsening the classification rules, namely, reasonably sacrifice the accuracy of description in a controlled level in order to improve the comprehensibility and predictability. The framework of granular computing provides us a formal and systematic methodology for doing this. In this paper, a modified PRISM classification algorithm is presented based on the framework of granular computing.