Decision Making Based on Hybrid of Multi-Knowledge and Naïve Bayes Classifier

Abstract

In general, knowledge can be represented by a mapping from a hypothesis space to a decision space. Usually, multiple mappings can be obtained from an instance information system. A set of mappings, which are created based on multiple reducts in the instance information system by means of rough set theory, is defined as multi-knowledge in this paper. Uncertain rules are introduced to represent multi-knowledge. A hybrid approach of multi-knowledge and the Naïve Bayes Classifier is proposed to make decisions for unseen instances or for instances with missing attribute values. The data sets from the UCI Machine Learning Repository are applied to test this decision-making algorithm. The experimental results show that the decision accuracies for unseen instances are higher than by using other approaches in a single body of knowledge.