Leveraging Association Rules for Better Predictions and Better Explanations

TL;DR

This paper introduces a method that uses association rules derived from data to enhance the accuracy and interpretability of tree-based classification models, improving predictions and explanations.

Contribution

It combines data mining and knowledge integration to boost predictive performance and generate more general abductive explanations for tree-based classifiers.

Findings

Improved predictive accuracy of decision trees and random forests.

Generation of more general and comprehensible explanations.

Demonstrated benefits in both prediction and explanation tasks.

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.