A Rule-Based Classification Algorithm: A Rough Set Approach

In this paper, we propose a rule-based classification algorithm named ROUSER (ROUgh SEt Rule). Researchers have proposed various classification algorithms and practitioners have applied them to various application domains, while most of the classification algorithms are designed with a focus on classification performance rather than interpretability or understandability of the models built using the algorithms. ROUSER is specifically designed to extract human understandable decision rules from nominal data. What distinguishes ROUSER from most, if not all, other rule-based classification algorithms is that it utilizes a rough set approach to decide an attribute-value pair for the antecedents of a rule. Moreover, the rule generation method of ROUSER is based on the separate-and-conquer strategy, and hence it is more efficient than the indiscernibility matrix method that is widely adopted in the classification algorithms based on the rough set theory. On about half of the data sets considered in experiments, ROUSER can achieve better classification performance than do classification algorithms that are able to generate decision rules or trees.