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Pattern classification by concurrently determined piecewise linear and convex discriminant functions

Authors
Ryoo, Hong Seo
Issue Date
9월-2006
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Keywords
machine learning; data mining; classification; mixed integer and linear programming
Citation
COMPUTERS & INDUSTRIAL ENGINEERING, v.51, no.1, pp.79 - 89
Indexed
SCIE
SCOPUS
Journal Title
COMPUTERS & INDUSTRIAL ENGINEERING
Volume
51
Number
1
Start Page
79
End Page
89
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/125931
DOI
10.1016/j.cie.2006.06.015
ISSN
0360-8352
Abstract
This paper develops a new methodology for pattern classification by concurrently determined k piecewise linear and convex discriminant functions. Toward the end, we design a new l(1)-norm distance metric for measuring misclassification errors and use it to develop a mixed 0-1 integer and linear program (MILP) for the k piecewise linear and convex separation of data. The proposed model is meritorious in that it considers the synergy as well as the individual role of the k hyperplanes in constructing a decision surface and exploits the advances in theory and algorithms and the advent of powerful softwares for MILP for its solution. With artificially created data, we illustrate pros and cons of pattern classification by the proposed methodology. With six benchmark classification datasets, we demonstrate that the proposed approach is effective and competitive with well-established learning methods. In summary, the classifiers constructed by the proposed approach obtain the best prediction rates on three of the six datasets and the second best records for two of the remaining three datasets. (c) 2006 Elsevier Ltd. All rights reserved.
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RYOO, Hong Seo
공과대학 (산업경영공학부)
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