Emmanouilidis, Christos and Hunter, Andrew and MacIntyre, John and Cox, Chris (1999) Selecting features in neurofuzzy modelling by multi-objective genetic algorithms. In: ICANN 1999, 7-10 September 1999, Edinburgh.
|Item Type:||Conference or Workshop Item (Paper)|
Restricted to Repository staff only
|Divisions:||College of Science > School of Computer Science|
|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.|
|Date Deposited:||25 Jun 2009 13:31|
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