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Merging subsets of attributes to improve a hybrid consistency-based filter: a case of study in product unit neural networks

2016; Taylor & Francis; Volume: 28; Issue: 3 Linguagem: Inglês

10.1080/09540091.2016.1149146

1360-0494

Antonio J. Tallón‐Ballesteros, José C. Riquelme, Roberto Ruíz,

Neural Networks and Applications

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.