Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Nonlinear separation of data via mixed 0-1 Integer and Linear Programming

Authors
Kim, KwangsooRyoo, Hong Seo
Issue Date
1-10월-2007
Publisher
ELSEVIER SCIENCE INC
Keywords
supervised learning; binary classification; mixed 0-1 integer and linear program; global optimization
Citation
APPLIED MATHEMATICS AND COMPUTATION, v.193, no.1, pp.183 - 196
Indexed
SCIE
SCOPUS
Journal Title
APPLIED MATHEMATICS AND COMPUTATION
Volume
193
Number
1
Start Page
183
End Page
196
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/125691
DOI
10.1016/j.amc.2007.03.067
ISSN
0096-3003
Abstract
This paper presents a new mathematical programming-based learning methodology for separation of two types of data. Specifically, we develop a new l(1)-norm error distance metric and use it to develop a Mixed 0-1 Integer and Linear Programming ( MILP) model that optimizes the interplay of user-provided discriminant functions, including kernel functions for support vector machines, to implement a nonlinear, nonconvex and/or disjoint decision boundary for the best separation of data at hand. With the concurrent optimization of discriminant functions, the MILP-based learning can be used for finding the optimal and least complex classification rule for noise-free data and for implementing a most robust classification rule for real-life data with noise. With extensive experiments on separation of two dimensional artificial datasets that are clean and noisy, we graphically illustrate the aforementioned advantages of the new MILP-based learning methodology. With experiments on real-life benchmark datasets from the UC Irvine Repository of machine learning databases, in comparison with the multisurface method and the support vector machines, we demonstrate the advantage of using and concurrently optimizing more than a single discriminant function for a robust separation of real-life data, hence the utility of the proposed methodology in supervised learning. (C) 2007 Elsevier Inc. All rights reserved.
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Engineering > School of Industrial and Management Engineering > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher RYOO, Hong Seo photo

RYOO, Hong Seo
공과대학 (산업경영공학부)
Read more

Altmetrics

Total Views & Downloads

BROWSE