Feature Selection in Associative Classification-A Review and Comparative Analysis
Raed Alazaidah, Ahmad Al–Qerem, Mais Haj Qasem, Ala’a Al-Shaikh, Nabeel Al-Milli, MohammadNoor Injadat
Abstract
Feature selection is one of the most important and significant steps that highly affects the final accuracy of any classification model. It aims to determine the most significant features that help in accurately predicting the class label for any new case. This research aims to survey and identify the best feature selection method that suits algorithms belong to Associative Classification (AC). Hence, nine different feature selection methods have been evaluated and compared using five AC based algorithms with respect to Accuracy metric. Results revealed that ABB-IEP and ABB-LIU methods show the best performance with datasets that contain nominal features only, while Relief feature selection method showed the best Accuracy with datasets that comprise Real and Integer features.