NDLI: A rough margin-based ν-twin support vector machine

Content Provider	Springer Nature Link
Author	Xu, Yitian Wang, Laisheng Zhong, Ping
Copyright Year	2011
Abstract	Twin support vector machine (TSVM) is a new machine learning algorithm, which aims at finding two nonparallel planes for each class. In order to do so, one needs to resolve a pair of smaller-sized quadratic programming problems (QPPs) rather than a single large one. However, when constructing the classification plane for one class, a large number of samples of this class are considered in the objective function, but only fewer samples in the other class are considered, which easily results in over-fitting problem. In addition, the same penalties are given to each misclassified samples in the TSVM. In fact, the misclassified samples have different effects on the decision of the hyper-plane. In order to overcome these two disadvantages, by introducing the rough set theory into ν-TSVM, we propose a rough margin-based ν-TSVM in this paper. In the proposed algorithm, the different points in the different positions are proposed to have different effects on the separating hyper-plane. We firstly construct rough lower margin, rough upper margin, and rough boundary in the ν-TSVM and then give the different penalties to the different misclassified samples according to their positions. The new classifier can avoid the over-fitting problem to a certain extent. Numerical experiments on one artificial dataset and six benchmark datasets demonstrate the feasibility and validity of the proposed algorithm.
Starting Page	1307
Ending Page	1317
Page Count	11
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	21
Issue Number	6
e-ISSN	14333058
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-03-05
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Rough margin Twin support vector machine ν support vector machine Nonparallel plane Simulation and Modeling
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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