A new rule ranking model for Associative Classification using a hybrid Artificial Intelligence technique
Moath Mustafa Ahmad Najeeb, Asim El Sheikh, Mohammed Nababteh · 2011
Rule ranking is a crucial step in Associative Classification (AC), AC algorithms proposed many ranking methods which aim to improve the accuracy of the classifier. In this paper we propose a new model in rule ranking, namely Hybrid-RuleRank, which employs a hybrid Artificial Intelligence (AI) technique that combines Simulated Annealing (SA) with Genetic Algorithm (GA), the new model tested against 11 data sets from UCI Machine Learning Repository, and the experimental results show that our model enhances the accuracy of the classifier.