Abstract
Recently, data mining is attracting researchers’ interest as a tool for getting knowledge from data bases on a large scale. Although there have been several approaches to data mining, we focus on mathematical programming (in particular, multi-objective and goal programming; MOP/GP) approaches in this paper. Among them, Support Vector Machine (SVM) is gaining popularity as a method for machine learning. In pattern classification problems with two class sets, its idea is to find a maximal margin separating hyperplane which gives the greatest separation between the classes in a high dimensional feature space. This task is performed by solving a quadratic programming problem in a traditional formulation, and can be reduced to solving a linear programming in another formulation. However, the idea of maximal margin separation is not quite new: in 1960’s the multi-surface method (MSM) was suggested by Mangasarian. In 1980’s, linear classifiers using goal programming were developed extensively.
This paper presents a survey on how effectively MOP/GP techniques can be applied to machine learning such as SVM, and discusses their problems.
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© 2003 Springer-Verlag Berlin Heidelberg
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Nakayama, H. (2003). MOP/GP Approaches to Data Mining. In: Multi-Objective Programming and Goal Programming. Advances in Soft Computing, vol 21. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36510-5_3
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DOI: https://doi.org/10.1007/978-3-540-36510-5_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00653-4
Online ISBN: 978-3-540-36510-5
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