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Binary classification posed as a quadratically constrained quadratic programming and solved using particle swarm optimization

Kumar, Deepak and Ramakrishnan, AG (2016) Binary classification posed as a quadratically constrained quadratic programming and solved using particle swarm optimization. In: SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES, 41 (3). pp. 289-298.

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Official URL: http://dx.doi.org/10.1007/s12046-016-0466-y

Abstract

Particle swarm optimization (PSO) is used in several combinatorial optimization problems. In this work, particle swarms are used to solve quadratic programming problems with quadratic constraints. The central idea is to use PSO to move in the direction towards optimal solution rather than searching the entire feasible region. Binary classification is posed as a quadratically constrained quadratic problem and solved using the proposed method. Each class in the binary classification problem is modeled as a multidimensional ellipsoid to form a quadratic constraint in the problem. Particle swarms help in determining the optimal hyperplane or classification boundary for a data set. Our results on the Iris, Pima, Wine, Thyroid, Balance, Bupa, Haberman, and TAE datasets show that the proposed method works better than a neural network and the performance is close to that of a support vector machine.

Item Type: Journal Article
Additional Information: Copy right for this article belongs to theINDIAN ACAD SCIENCES, C V RAMAN AVENUE, SADASHIVANAGAR, P B #8005, BANGALORE 560 080, INDIA
Keywords: Quadratic programming; particle swarm optimization; hyperplane; quadratic constraints; binary classification
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Depositing User: Id for Latest eprints
Date Deposited: 11 Jun 2016 09:11
Last Modified: 11 Jun 2016 09:11
URI: http://eprints.iisc.ac.in/id/eprint/53917

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