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

By Christos Emmanouilidis, Andrew Hunter and John MacIntyre

Abstract

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

Topics: G730 Neural Computing
Year: 2000
DOI identifier: 10.1109/CEC.2000.870311
OAI identifier: oai:eprints.lincoln.ac.uk:1907
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