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Mathematical Programming Methods of Pattern Classification

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  • Richard C. Grinold

    (University of California, Berkeley)

Abstract

This survey examines eight mathematical programming models of pattern classification. The paper determines the range of applicability and computational merits of each model. The flexibility and large sample properties of the models are also discussed. We find that six of the models produce decision rules that maximize a function we call the quality of a decision rule. The remaining two models minimize a weighted sum of errors.

Suggested Citation

  • Richard C. Grinold, 1972. "Mathematical Programming Methods of Pattern Classification," Management Science, INFORMS, vol. 19(3), pages 272-289, November.
    • Handle: RePEc:inm:ormnsc:v:19:y:1972:i:3:p:272-289
    DOI: 10.1287/mnsc.19.3.272
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    Cited by:

    1. Balaji Padmanabhan & Alexander Tuzhilin, 2003. "On the Use of Optimization for Data Mining: Theoretical Interactions and eCRM Opportunities," Management Science, INFORMS, vol. 49(10), pages 1327-1343, October.
    2. Pedro Duarte Silva, A., 2017. "Optimization approaches to Supervised Classification," European Journal of Operational Research, Elsevier, vol. 261(2), pages 772-788.
    3. Nieddu, Luciano & Patrizi, Giacomo, 2000. "Formal methods in pattern recognition: A review," European Journal of Operational Research, Elsevier, vol. 120(3), pages 459-495, February.
    4. 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.
    5. Ryu, Young U. & Chandrasekaran, R. & Jacob, Varghese S., 2007. "Breast cancer prediction using the isotonic separation technique," European Journal of Operational Research, Elsevier, vol. 181(2), pages 842-854, September.

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