Improved Instance Selection Methods for Support Vector Machine Speed Optimization
Joint Authors
Akinyelu, Andronicus A.
Adewumi, Aderemi Oluyinka
Source
Security and Communication Networks
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-01-09
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Akinyelu, Andronicus A.& Adewumi, Aderemi Oluyinka. 2017. Improved Instance Selection Methods for Support Vector Machine Speed Optimization. Security and Communication Networks،Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1203070
Modern Language Association (MLA)
Akinyelu, Andronicus A.& Adewumi, Aderemi Oluyinka. Improved Instance Selection Methods for Support Vector Machine Speed Optimization. Security and Communication Networks No. 2017 (2017), pp.1-11.
https://search.emarefa.net/detail/BIM-1203070
American Medical Association (AMA)
Akinyelu, Andronicus A.& Adewumi, Aderemi Oluyinka. Improved Instance Selection Methods for Support Vector Machine Speed Optimization. Security and Communication Networks. 2017. Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1203070
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1203070