A multiobjective evolutionary setting for feature selection and a commonality-based crossover operator

Emmanouilidis, Christos and Hunter, Andrew and MacIntyre, John (2000) A multiobjective evolutionary setting for feature selection and a commonality-based crossover operator. In: 2000 Congress on Evolutionary Computation, 16th-19th July 2000, La Jolla, CA, USA.

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A multiobjective evolutionary setting for feature selection and a commonality-based crossover operator
A Multiobjective Evolutionary Setting for Feature Selection and a Commonality-Based Crossover Operator
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Full text URL: http://dx.doi.org/10.1109/CEC.2000.870311

Abstract

Abstract- Feature subset selection is a common and key
problem in many classification and regression tasks. It can
be viewed as a multi-objective optimisation problem, since,
in the simplest case, it involves feature subset size
minimisation and performance maximisation. Here, a
multiobjective evolutionary approach is proposed for
feature selection. A novel commonality-based crossover
operator is introduced and placed in the multiobjective
evolutionary setting. This specialised operator helps to
preserve building blocks with promising performance. The
multiobjective evolutionary algothim employs the novel
crossover operator in order to evolve a diverse population of feature subsets with different subset size/performance
trade-offs. Selection bias reduction is achieved by means of
resampling. We argue that this is a generic approach,
which can be used in many modelling problems. It is applied
to feature selection on different neural network
architectures. Results from experiments with high
dimensional benchmarking data sets are given.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Abstract- Feature subset selection is a common and key problem in many classification and regression tasks. It can be viewed as a multi-objective optimisation problem, since, in the simplest case, it involves feature subset size minimisation and performance maximisation. Here, a multiobjective evolutionary approach is proposed for feature selection. A novel commonality-based crossover operator is introduced and placed in the multiobjective evolutionary setting. This specialised operator helps to preserve building blocks with promising performance. The multiobjective evolutionary algothim employs the novel crossover operator in order to evolve a diverse population of feature subsets with different subset size/performance trade-offs. Selection bias reduction is achieved by means of resampling. We argue that this is a generic approach, which can be used in many modelling problems. It is applied to feature selection on different neural network architectures. Results from experiments with high dimensional benchmarking data sets are given.
Keywords:Algorithms, Feature selection, Multiobjective evolutionary algorithm, Neural networks
Subjects:G Mathematical and Computer Sciences > G730 Neural Computing
Divisions:College of Science > School of Computer Science
ID Code:1907
Deposited By: Tammie Farley
Deposited On:23 Jun 2009 14:55
Last Modified:13 Mar 2013 08:32

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