An interpretable classification rule mining algorithm

Obtaining comprehensible classifiers may be as important as achieving high accuracy in many real-life applications such as knowledge discovery tools and decision support systems. This paper introduces an efficient Evolutionary Programming algorithm for solving classification problems by means of very interpretable and comprehensible IF-THEN classification rules. This algorithm, called the Interpretable Classification Rule Mining (ICRM) algorithm, is designed to maximize the comprehensibility of the classifier by minimizing the number of rules and the number of conditions. The evolutionary process is conducted to construct classification rules using only relevant attributes, avoiding noisy and redundant data information. The algorithm is evaluated and compared to nine other well-known classification techniques in 35 varied application domains. Experimental results are validated using several non-parametric statistical tests applied on multiple classification and interpretability metrics. The experiments show that the proposal obtains good results, improving significantly the interpretability measures over the rest of the algorithms, while achieving competitive accuracy. This is a significant advantage over other algorithms as it allows to obtain an accurate and very comprehensible classifier quickly.