Hybrid Feature Selection based Classifier for Non Functional Requirements

Abstract

Feature Selection aims at finding the most relevant features of a problem domain. It is very helpful in improving computational speed and prediction accuracy. However, identification of useful features from hundreds or even thousands of related features is a nontrivial task. In this paper, we introduce a hybrid FS method which combines two FS methods – the filters and the wrappers for NFRs classification. Candidate features are first selected from the original feature set through two filter techniques then the union of these two sets are further refined by more accurate wrappers. This hybrid mechanism takes advantage of both the filters and the wrappers. The mechanism is examined by primary dataset based on NFRs. Experimental results show that better prediction accuracy can be achieved with a smaller feature set.