Angle-based twin support vector machine

Khemchandani, Reshma

doi:10.1007/s10479-017-2604-2

Angle-based twin support vector machine

2017-08-21

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Khemchandani, Reshma *

P

Pooja Saigal

DOI：10.1007/s10479-017-2604-2

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In this paper, a novel nonparallel hyperplane based classifier termed as angle-based twin support vector machine (ATWSVM) has been proposed which is motivated by the concept of twin support vector machine (TWSVM). TWSVM obtains two nonparallel hyperplanes by solving a pair of quadratic programming problems (QPPs). ATWSVM presents a generic classification model, where the first problem can be formulated using a TWSVM-based classifier and the second problem is an unconstrained minimization problem (UMP) which is reduced to solving a system of linear equations. The second hyperplane is determined so that it is proximal to its own class and the angle between the normals to the two hyperplanes is maximized. The notion of angle has been introduced to have maximum separation between the two hyperplanes. In this work, we have presented two versions of ATWSVM: one that solves a QPP and a UMP; second which formulates both the problems as UMPs. The training time of ATWSVM is much less than that of TWSVM because ATWSVM solves the second problem as UMP instead of QPP. To test the efficacy of the proposed classifier, experiments have been conducted on synthetic and benchmark datasets and it is observed that the proposed classifier achieves classification accuracy comparable or better than that of TWSVM. This work also proposes application of ATWSVM for color image segmentation.

Keyword:

Angle-based twin support vector machine

Supervised learning

Unconstrained minimization problem

Angle-based twin support vector machine

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