Partially monotonic decision trees

Abstract

In multicriteria decision tasks, certain features are linearly ordered according to the decision and are called criteria, whereas others, called regular attributes, are not. In practice, regular attributes and criteria coexist in most classification tasks. In this paper, we propose a rank-inconsistent rate that distinguishes attributes from criteria. Furthermore, it represents the directions of the monotonic relationships between criteria and decisions. We design a partially monotonic decision tree algorithm to extract decision rules for partially monotonic classification tasks. Experimental results show that the proposed algorithm is effective and efficient.