Separation of data via concurrently determined discriminant functions

Hong Seo Ryoo, Kwangsoo Kim

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    This paper presents a mixed 0-1 integer and linear programming (MILP) model for separation of data via a finite number of nonlinear and nonconvex discriminant functions. The MILP model concurrently optimizes the parameters of the user-provided individual discriminant functions and implements a decision boundary for an optimal separation of data under analysis. The MILP model is extensively tested on six well-studied datasets in data mining research. The comparison of numerical results by the MILP-based classification of data with those produced by the multisurface method and the support vector machine in these experiments and the best from the literature illustrates the efficacy and the usefulness of the new MILP-based classification of data for supervised learning.

    Original languageEnglish
    Title of host publicationTheory and Applications of Models of Computation - 4th International Conference, TAMC 2007, Proceedings
    PublisherSpringer Verlag
    Pages533-541
    Number of pages9
    ISBN (Print)3540725032, 9783540725039
    DOIs
    Publication statusPublished - 2007
    Event4th International Conference on Theory and Applications of Models of Computation, TAMC 2007 - Shanghai, China
    Duration: 2007 May 222007 May 25

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume4484 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other4th International Conference on Theory and Applications of Models of Computation, TAMC 2007
    Country/TerritoryChina
    CityShanghai
    Period07/5/2207/5/25

    Keywords

    • Data classification
    • Machine learning
    • Mixed integer and linear programming

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science

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