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 publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages533-541
Number of pages9
Volume4484 LNCS
Publication statusPublished - 2007 Oct 29
Event4th International Conference on Theory and Applications of Models of Computation, TAMC 2007 - Shanghai, China
Duration: 2007 May 222007 May 25

Other

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

Fingerprint

Linear Programming
0-1 Integer Programming
Discriminant Function
Mixed Integer Programming
Integer programming
Linear programming
Programming Model
Linear Models
Linear Model
Data Mining
Supervised learning
Supervised Learning
Support vector machines
Data mining
Efficacy
Support Vector Machine
Optimise
Learning
Numerical Results
Research

Keywords

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

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Ryoo, H. S., & Kim, K. (2007). Separation of data via concurrently determined discriminant functions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4484 LNCS, pp. 533-541)

Separation of data via concurrently determined discriminant functions. / Ryoo, Hong Seo; Kim, Kwangsoo.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4484 LNCS 2007. p. 533-541.

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

Ryoo, HS & Kim, K 2007, Separation of data via concurrently determined discriminant functions. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 4484 LNCS, pp. 533-541, 4th International Conference on Theory and Applications of Models of Computation, TAMC 2007, Shanghai, China, 07/5/22.
Ryoo HS, Kim K. Separation of data via concurrently determined discriminant functions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4484 LNCS. 2007. p. 533-541
Ryoo, Hong Seo ; Kim, Kwangsoo. / Separation of data via concurrently determined discriminant functions. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4484 LNCS 2007. pp. 533-541
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