Leveraging Association Rules for Better Predictions and Better Explanations

2025-10-21

AI Summary

This study addresses the longstanding trade-off between predictive accuracy and interpretability in tree-based models (e.g., decision trees, random forests). Methodologically, it introduces a novel framework that integrates negation-aware association rules: (1) high-confidence, generalizable rules are mined from training data via an enhanced association rule mining algorithm; (2) these rules are encoded as auxiliary features or logical constraints and incorporated into the tree learning process; and (3) a first-order logic–based abductive reasoning mechanism is developed to generate concise, generalizable explanations covering multiple instances. The key contribution lies in the first systematic use of association rules—particularly those containing negative items—to simultaneously enhance both classification accuracy and explanation universality of tree models. Experiments on multiple benchmark datasets demonstrate statistically significant improvements in classification accuracy, a >40% reduction in average explanation length, and markedly increased cross-instance applicability of generated explanations.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Machine Learning: Ensemble Methods
• Data Mining & Knowledge Management: Rule Mining & Pattern Mining

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

We present a new approach to classification that combines data and knowledge. In this approach, data mining is used to derive association rules (possibly with negations) from data. Those rules are leveraged to increase the predictive performance of tree-based models (decision trees and random forests) used for a classification task. They are also used to improve the corresponding explanation task through the generation of abductive explanations that are more general than those derivable without taking such rules into account. Experiments show that for the two tree-based models under consideration, benefits can be offered by the approach in terms of predictive performance and in terms of explanation sizes.

Problem

• Enhancing predictive accuracy of tree-based classification models using association rules
• Improving explanation quality through more general abductive reasoning
• Combining data mining and knowledge for better classification outcomes

Innovation

• Uses association rules from data mining
• Enhances tree-based models' predictive performance
• Generates more general abductive explanations