Concise rule induction algorithm based on one-sided maximum decision tree approach

As the importance of machine learning tools for decision support continues to grow, interpretability has emerged as a key factor. Rule-based classification algorithms, such as decision trees and rule induction, enable high local interpretability by providing transparent reasoning rules in an IF-THEN format. In this context, it is essential to provide concise and clear rules and conditions to achieve high local interpretability. This study proposes a novel Concise Algorithm, designed to effectively remove irrelevant conditions from classification rules. We present a framework incorporating the Concise Algorithm, which employs the One-Sided-Maximum decision tree algorithm for rule generation, followed by the application of the Concise Algorithm to remove irrelevant conditions. This proposed framework produces a rule-based classification model that exhibits an enhanced predictive performance-interpretability trade-off compared to benchmark methods (CART, Ripper, CN2, and modified One-Sided-Maximum), as demonstrated by empirical tests conducted on 19 UCI datasets. A case study focusing on the breast-cancer-wisconsin dataset provides a comprehensive analysis of the rule and condition generation processes.