A Bayesian decision theory approach to variable selection for discrimination

  • Authors:

  • T. Fearn;P. J. Brown;P. Besbeas

  • Affiliations:

  • Department of Statistical Science, University College London, Gower Street, London WC1E 6BT, UK. Tom@stats.ucl.ac.uk;Institute of Mathematics and Statistics, University of Kent at Canterbury, Cornwallis Building, Canterbury, Kent CT2 7NF, UK;Institute of Mathematics and Statistics, University of Kent at Canterbury, Cornwallis Building, Canterbury, Kent CT2 7NF, UK

  • Venue:
  • Statistics and Computing
  • Year:
  • 2002

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Abstract

Motivated by examples in spectroscopy, we study variable selection for discrimination in problems with very many predictor variables. Assuming multivariate normal distributions with common variance for the predictor variables within groups, we develop a Bayesian decision theory approach that balances costs for variables against a loss due to classification errors. The approach is computationally intensive, requiring a simulation to approximate the intractable expected loss and a search, using simulated annealing, over a large space of possible subsets of variables. It is illustrated by application to a spectroscopic example with 3 groups, 100 variables, and 71 training cases, where the approach finds subsets of between 5 and 14 variables whose discriminatory power is comparable with that of linear discriminant analysis using principal components derived from the full 100 variables. We study both the evaluation of expected loss and the tuning of the simulated annealing for the example, and conclude that computational effort should be concentrated on the search.