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
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