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Heuristics for efficient classification

Author

Listed:
  • Kathryn Fraughnaugh
  • Jennifer Ryan
  • Holly Zullo
  • Louis Cox

Abstract

The classification problem is to determine the class of an object when it is costly to observe the values of its attributes. This type of problem arises in fault diagnosis, in the desig- of interactive expert systems, in reliability analysis of coherent systems, in discriminant analysis of test data, and in many other applications. We introduce a generic decision rule that specifies the next attribute to test at any location in a decision tree. Random searches and tabu searches are applied to determine the best specific form of the rule. The most successful heuristics that we developed are based on the tabu search paradigm. We present computational results for problems with a variety of characteristics and compare our heuristics to an exact dynamic programming algorithm. Copyright Kluwer Academic Publishers 1998

Suggested Citation

  • Kathryn Fraughnaugh & Jennifer Ryan & Holly Zullo & Louis Cox, 1998. "Heuristics for efficient classification," Annals of Operations Research, Springer, vol. 78(0), pages 189-200, January.
    • Handle: RePEc:spr:annopr:v:78:y:1998:i:0:p:189-200:10.1023/a:1018997900011
    DOI: 10.1023/A:1018997900011
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    Cited by:

    1. Mingue Sun, 2009. "Liquidity Risk and Financial Competition: A Mixed Integer Programming Model for Multiple-Class Discriminant Analysis," Working Papers 0102, College of Business, University of Texas at San Antonio.
    2. Zopounidis, Constantin & Doumpos, Michael, 2002. "Multicriteria classification and sorting methods: A literature review," European Journal of Operational Research, Elsevier, vol. 138(2), pages 229-246, April.
    3. Mingue Sun, 2009. "Liquidity Risk and Financial Competition: A Mixed Integer Programming Model for Multiple-Class Discriminant Analysis," Working Papers 0102, College of Business, University of Texas at San Antonio.

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