Ranked Multi-Label Rules Associative Classifier

Abstract

Associative classification is a promising approach in data mining, which integrates association rule discovery and classification. In this paper, we present a novel associative classification technique called Ranked Multilabel Rule (RMR) that derives rules with multiple class labels. Rules derived by current associative classification algorithms overlap in their training data records, resulting in many redundant and useless rules. However, RMR removes the overlapping between rules using a pruning heuristic and ensures that rules in the final classifier do not share training records, resulting in more accurate classifiers. Experimental results obtained on twenty data sets show that the classifiers produced by RMR are highly competitive if compared with those generated by decision trees and other popular associative techniques such as CBA, with respect to prediction accuracy.