Paper Info
Title
Merging subsets of attributes to improve a hybrid consistency-based filter: a case of study in product unit neural networks.
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.
Year
DOI
Venue
2016
10.1080/09540091.2016.1149146
Connect. Sci.
Keywords
Field
DocType
Artificial neural networks,feature selection,classification,product units,filters,feature subset selection
Data mining,Evolutionary algorithm,Feature selection,Computer science,Artificial intelligence,Merge (version control),Artificial neural network,Evolutionary programming,Statistical hypothesis testing,Pattern recognition,Correlation,Quality enhancement,Machine learning
Journal
Volume
Issue
ISSN
28
3
0954-0091
Citations 
PageRank 
References 
0
0.34
3
Authors
3
Name
Order
Citations
PageRank
Antonio J. Tallón-Ballesteros1327.66
José Cristóbal Riquelme Santos231842.86
Roberto Ruiz Sánchez313.07