Location of Repository

Selecting features in neurofuzzy modelling by multi-objective genetic algorithms

By Christos Emmanouilidis, Andrew Hunter, John MacIntyre and Chris Cox

Abstract

ABSTRACT\ud Empirical modelling in high dimensional spaces is usually preceded by a feature selection stage. Irrelevant or noisy features unnecessarily increase the complexity of the\ud problem and can degrade modelling performance. Here, multiobjective genetic algorithms, are proposed, as effective means of evolving a diverse population of alternative feature sets with various accuracy/complexity\ud trade-offs. They are shown to be particularly successful in neurofuzzy modelling, in conjunction with a method for performing fast fitness evaluation. The major contributions of this paper are in the use of a specific type of\ud multiobjective genetic algorithm, based on the concept of dominance, for feature selection; and the combination of fast fitness evaluation of neurofuzzy models with a genetic algorithm. The effectiveness of the proposed approach is\ud demonstrated on two high dimensional regression problems

Topics: G760 Machine Learning
Year: 1999
OAI identifier: oai:eprints.lincoln.ac.uk:1898
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://eprints.lincoln.ac.uk/1... (external link)
  • http://anc.ed.ac.uk/ICANN99/ (external link)
  • http://eprints.lincoln.ac.uk/1... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.