Genetic wrappers for feature selection in decision tree induction and variable ordering in Bayesian network structure learning

  • Authors:

  • William H. Hsu

  • Affiliations:

  • Department of Computing and Information Sciences, Kansas State University, Manhattan, KS and National Center for Supercomputing Applications (NCSA), University of Illinois, Champaign, IL

  • Venue:
  • Information Sciences: an International Journal - Special issue: Soft computing data mining
  • Year:
  • 2004

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Abstract

In this paper, we address the automated tuning of input specification for supervised inductive learning and develop combinatorial optimization solutions for two such tuning problems. First, we present a framework for selection and reordering of input variables to reduce generalization error in classification and probabilistic inference. One purpose of selection is to control overfitting using validation set accuracy as a criterion for relevance. Similarly, some inductive learning algorithms, such as greedy algorithms for learning probabilistic networks, are sensitive to the evaluation order of variables. We design a generic fitness function for validation of input specification, then use it to develop two genetic algorithm wrappers: one for the variable selection problem for decision tree inducers and one for the variable ordering problem for Bayesian network structure learning. We evaluate the wrappers, using real-world data for the selection wrapper and synthetic data for both, and discuss their limitations and generalizability to other inducers.