Role of Interestingness Measures in CAR Rule Ordering for Associative Classifier: An Empirical Approach

TL;DR

This paper investigates how various interestingness measures influence the ordering and selection of class association rules in associative classifiers, aiming to improve model relevance and performance.

Contribution

It introduces an empirical analysis of different interestingness measures' impact on CAR rule ordering and selection in associative classifiers.

Findings

Different interestingness measures significantly affect rule selection.

Hybrid ordering methods can enhance classifier performance.

Empirical results demonstrate improved accuracy with certain measures.

Abstract

Associative Classifier is a novel technique which is the integration of Association Rule Mining and Classification. The difficult task in building Associative Classifier model is the selection of relevant rules from a large number of class association rules (CARs). A very popular method of ordering rules for selection is based on confidence, support and antecedent size (CSA). Other methods are based on hybrid orderings in which CSA method is combined with other measures. In the present work, we study the effect of using different interestingness measures of Association rules in CAR rule ordering and selection for associative classifier.