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Security and Communication Networks
Volume 2017 (2017), Article ID 6790975, 11 pages
https://doi.org/10.1155/2017/6790975
Research Article

Improved Instance Selection Methods for Support Vector Machine Speed Optimization

School of Mathematics, Statistics & Computer Science, University of KwaZulu-Natal, Private Bag X54001, Durban 4000, South Africa

Correspondence should be addressed to Aderemi O. Adewumi; moc.liamg@jtmeral

Received 1 July 2016; Accepted 22 August 2016; Published 9 January 2017

Academic Editor: Pascal Lorenz

Copyright © 2017 Andronicus A. Akinyelu and Aderemi O. Adewumi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Support vector machine (SVM) is one of the top picks in pattern recognition and classification related tasks. It has been used successfully to classify linearly separable and nonlinearly separable data with high accuracy. However, in terms of classification speed, SVMs are outperformed by many machine learning algorithms, especially, when massive datasets are involved. SVM classification speed scales linearly with number of support vectors, and support vectors increase with increase in dataset size. Hence, SVM classification speed can be enormously reduced if it is trained on a reduced dataset. Instance selection techniques are one of the most effective techniques suitable for minimizing SVM training time. In this study, two instance selection techniques suitable for identifying relevant training instances are proposed. The techniques are evaluated on a dataset containing 4000 emails and results obtained compared to other existing techniques. Result reveals excellent improvement in SVM classification speed.