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 language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 533-541 |
| Number of pages | 9 |
| Volume | 4484 LNCS |
| Publication status | Published - 2007 Oct 29 |
| Event | 4th International Conference on Theory and Applications of Models of Computation, TAMC 2007 - Shanghai, China Duration: 2007 May 22 → 2007 May 25 |
Other
| Other | 4th International Conference on Theory and Applications of Models of Computation, TAMC 2007 |
|---|---|
| Country | China |
| City | Shanghai |
| Period | 07/5/22 → 07/5/25 |
Fingerprint
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
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 proceeding › Conference contribution
}
TY - GEN
T1 - Separation of data via concurrently determined discriminant functions
AU - Ryoo, Hong Seo
AU - Kim, Kwangsoo
PY - 2007/10/29
Y1 - 2007/10/29
N2 - 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.
AB - 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.
KW - Data classification
KW - Machine learning
KW - Mixed integer and linear programming
UR - http://www.scopus.com/inward/record.url?scp=35448985693&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=35448985693&partnerID=8YFLogxK
M3 - Conference contribution
SN - 3540725032
SN - 9783540725039
VL - 4484 LNCS
SP - 533
EP - 541
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -