Merging subsets of attributes to improve a hybrid consistency-based filter: a case of study in product unit neural networks. Issue 3 (2nd July 2016)

• Record Type: Journal Article
• Authors: Tallón-Ballesteros, A. J.; Riquelme, J. C.; Ruiz, R.
• Is Part Of: Connection science. Volume 28:Issue 3(2016)
• Page Start: 242
• Page End: 257
• Publication Date: 2016-07-02
• Subjects: Artificial neural networks -- feature selection -- classification -- product units -- filters -- feature subset selection; Neural computers -- Periodicals; Artificial intelligence -- Periodicals; Cognitive science -- Periodicals; Connectionism -- Periodicals; 006.3
• DOI: 10.1080/09540091.2016.1149146
• Languages: English
• ISSNs: 0954-0091

Abstract

ABSTRACT: This paper presents a quality enhancement of the selected features by a hybrid filter-based jointly on feature ranking and feature subset selection (FR-FSS) using a consistency-based measure via merging new features which are obtained applying other FR-FSS evaluated with a correlation metric. The goal is to overcome the accuracy of a neural network classifier containing product units as hidden nodes combined with a feature selection pre-processing step by means of a single consistency-based FR-FSS filter. Neural models are trained with a refined evolutionary programming approach called two-stage evolutionary algorithm. The experimentation has been carried out in eight complex classification problems, seven out of them from UCI (University of California at Irvine) repository and one real-world problem, with high test error rates (around 20%) with powerful classifiers such as 1-nearest neighbour or C4.5. Non-parametric statistical tests revealed that the new proposal significantly improves the accuracy of the neural models.