Data separation via a finite number of discriminant functions: A global optimization approach
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Kwangsoo | - |
dc.contributor.author | Ryoo, Hong Seo | - |
dc.date.accessioned | 2021-09-09T17:13:38Z | - |
dc.date.available | 2021-09-09T17:13:38Z | - |
dc.date.created | 2021-06-10 | - |
dc.date.issued | 2007-07-01 | - |
dc.identifier.issn | 0096-3003 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/125744 | - |
dc.description.abstract | This paper presents a mixed 0-1 integer and linear programming (MILP) model for separation of data via a finite number of non-linear and non-convex discriminant functions. The MILP model concurrently optimizes the parameters of the user-provided individual discriminant functions to implement a decision boundary for an optimal separation of data under analysis. The performance of the MILP-based classification of data is illustrated on randomly generated two dimensional datasets and extensively tested on six well-studied datasets in data mining research, in comparison with three well-established supervised learning methodologies, namely, the multisurface method, the logical analysis of data and the support vector machines. Numerical results from these experiments show that the new MILP-based classification of data is an effective and useful methodology for supervised learning. (c) 2007 Elsevier Inc. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCIENCE INC | - |
dc.subject | MULTISURFACE METHOD | - |
dc.subject | PATTERN SEPARATION | - |
dc.subject | CLASSIFICATION | - |
dc.subject | SETS | - |
dc.title | Data separation via a finite number of discriminant functions: A global optimization approach | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Ryoo, Hong Seo | - |
dc.identifier.doi | 10.1016/j.amc.2007.01.051 | - |
dc.identifier.scopusid | 2-s2.0-34249866594 | - |
dc.identifier.wosid | 000247803500045 | - |
dc.identifier.bibliographicCitation | APPLIED MATHEMATICS AND COMPUTATION, v.190, no.1, pp.476 - 489 | - |
dc.relation.isPartOf | APPLIED MATHEMATICS AND COMPUTATION | - |
dc.citation.title | APPLIED MATHEMATICS AND COMPUTATION | - |
dc.citation.volume | 190 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 476 | - |
dc.citation.endPage | 489 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Mathematics, Applied | - |
dc.subject.keywordPlus | MULTISURFACE METHOD | - |
dc.subject.keywordPlus | PATTERN SEPARATION | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | SETS | - |
dc.subject.keywordAuthor | data classification | - |
dc.subject.keywordAuthor | supervised learning | - |
dc.subject.keywordAuthor | mixed 0-1 and linear programming | - |
dc.subject.keywordAuthor | global optimization | - |
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