Knowledge based Least Squares Twin support vector machines

We propose knowledge based versions of a relatively new family of SVM algorithms based on two non-parallel hyperplanes. Specifically, we consider prior knowledge in the form of multiple polyhedral sets and incorporate the same into the formulation of linear Twin SVM (TWSVM)/Least Squares Twin SVM (L...

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Bibliographic Details
Published in:Information sciences 2010-12, Vol.180 (23), p.4606-4618
Main Authors: Arun Kumar, M., Khemchandani, Reshma, Gopal, M., Chandra, Suresh
Format: Article
Language:English
Subjects:
Algorithms
Classifiers
Formulations
Hyperplanes
Knowledge base
Knowledge based systems
Least squares method
Mathematical models
Pattern classification
Support vector machines
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Summary:We propose knowledge based versions of a relatively new family of SVM algorithms based on two non-parallel hyperplanes. Specifically, we consider prior knowledge in the form of multiple polyhedral sets and incorporate the same into the formulation of linear Twin SVM (TWSVM)/Least Squares Twin SVM (LSTWSVM) and term them as knowledge based TWSVM (KBTWSVM)/knowledge based LSTWSVM (KBLSTWSVM). Both of these formulations are capable of generating non-parallel hyperplanes based on real-world data and prior knowledge. We derive the solution of KBLSTWSVM and use it in our computational experiments for comparison against other linear knowledge based SVM formulations. Our experiments show that KBLSTWSVM is a versatile classifier whose solution is extremely simple when compared with other linear knowledge based SVM algorithms.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2010.07.034