Emmanouilidis, Christos, Hunter, Andrew, MacIntyre, John and Cox, Chris (1999) Selecting features in neurofuzzy modelling by multi-objective genetic algorithms. In: ICANN 1999, 7-10 September 1999, Edinburgh.
Full content URL: http://anc.ed.ac.uk/ICANN99/
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| Item Type: | Conference or Workshop contribution (Paper) |
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| Item Status: | Live Archive |
Abstract
ABSTRACT
Empirical modelling in high dimensional spaces is usually preceded by a feature selection stage. Irrelevant or noisy features unnecessarily increase the complexity of the
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
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
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
demonstrated on two high dimensional regression problems.
| Additional Information: | ABSTRACT Empirical modelling in high dimensional spaces is usually preceded by a feature selection stage. Irrelevant or noisy features unnecessarily increase the complexity of the 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 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 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 demonstrated on two high dimensional regression problems. |
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| Keywords: | Genetic algorithms, Algorithms, Neurofuzzy |
| Subjects: | G Mathematical and Computer Sciences > G760 Machine Learning |
| Divisions: | College of Science > School of Computer Science |
| ID Code: | 1898 |
| Deposited On: | 25 Jun 2009 13:31 |
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