Multi-Soft Set Based Approach for Rule based Classification Using Sequential Covering Algorithm

Abstract

Rule-based classification is a popular and adaptable method for determining the class of an object. Two accepted rule based classification methods are decision tree induction and sequential covering algorithms. Now a days, soft computing tools are widely used in all areas of knowledge discovery. Soft set theory that has wide application in data mining, is a novel soft computing tool. One of its branches is multi soft set theory. In the research, we have brought out applications of multi soft set theory in rule-based classification using a sequential covering algorithm. Conventional sequential covering algorithm for implementing rule based classification has certain limitations. The proposed research is supposed to overcome those problems. The beginning part is an introduction and the second part, details the applications of multi-soft set theory. Towards the end, we are contributing a new algorithm using multi soft set theory which can be implemented in python programming language. This new method has wide applications in different areas like business, health, sports, education etc.