CAR-NF +: An improved version of CAR-NF classifier

Abstract

In this paper, we propose two improvements to CAR-NF classifier, which is a classifier based on Class Association Rules (CARs). The first one, is a theoretical proof that allows selecting the minimum Netconf threshold, independently of the dataset, that avoids ambiguity at the classification stage. The second one, is a new coverage criterion, which aims to reduce the number of non-covered unseen-transactions during the classification stage. Experiments over several datasets show that the improved classifier, called CAR-NF +, beats the best reported classifiers based on CARs, including the original CAR-NF classifier. © 2012 Springer-Verlag.