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논문 기본 정보

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한국경영과학회 한국경영과학회 학술대회논문집 한국경영과학회 2005년 추계학술대회 및 정기총회
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    초록·키워드

    In this paper, we deal with the separation of data by concurrently determined, piecewise nonlinear and nonconvex discriminant functions. Toward the end, we develop a new l₁-distance norm error metric and cast the problem as a mixed 0-1 integer and linear programming(MILP) model. Given a finite number of discriminant functions as an input, the proposed model fully considers the synergy as well as the individual role of the functions involved and implements a simplest nonlinear and nonconvex decision surface that best separates the data on hand. Hence, exploiting powerful MILP solution packages for solution, the model efficiently analyzes any given data set for its piecewise nonlinear and nonconvex separability. With the classification of artificially created two dimensional data, we demonstrate the major strength of the proposed model that is implements a simplest decision surface for a dataset with a nonlinear and noconvex oundary between the two types of its members.

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      UCI(KEPA) : I410-ECN-0101-2009-325-017270925