CAR-NF: A classifier based on specific rules with high netconf

Abstract

In this paper, an accurate classifier based on Class Association Rules (CARs), called CAR-NF, is proposed. CAR-NF introduces a new strategy for computing CARs, using the Netconf as measure of interest, that allows to prune the CAR search space for building specific rules with high Netconf. Moreover, we propose and prove a proposition that supports the use of a Netconf threshold value equal to 0.5 for mining the CARs. Additionally, a new way for ordering the set of CARs based on their rule sizes and Netconf values is introduced in CAR-NF. The ordering strategy together with the "Best K rules" satisfaction mechanism allows CAR-NF to have better accuracy than CBA, CMAR, CPAR, TFPC and HARMONY classifiers, the best classifiers based on CARs reported in the literature.