Article

Support vector machines training data selection using a genetic algorithm

Authors:

Michal Kawulok,

Jakub NalepaAuthors Info & Claims

SSPR'12/SPR'12: Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition

Pages 557 - 565

https://doi.org/10.1007/978-3-642-34166-3_61

Published: 07 November 2012 Publication History

Abstract

This paper presents a new method for selecting valuable training data for support vector machines (SVM) from large, noisy sets using a genetic algorithm (GA). SVM training data selection is a known, however not extensively investigated problem. The existing methods rely mainly on analyzing the geometric properties of the data or adapt a randomized selection, and to the best of our knowledge, GA-based approaches have not been applied for this purpose yet. Our work was inspired by the problems encountered when using SVM for skin segmentation. Due to a very large set size, the existing methods are too time-consuming, and random selection is not effective because of the set noisiness. In the work reported here we demonstrate how a GA can be used to optimize the training set, and we present extensive experimental results which confirm that the new method is highly effective for real-world data.

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Cited By

Birzhandi PKim KYoun H(2022)Reduction of training data for support vector machine: a surveySoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-06787-526:8(3729-3742)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s00500-022-06787-5
Grzejszczak TKawulok MGaluszka A(2016)Hand landmarks detection and localization in color imagesMultimedia Tools and Applications10.1007/s11042-015-2934-575:23(16363-16387)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1007/s11042-015-2934-5
Cwiek MNalepa JArnold D(2014)A fast genetic algorithm for the flexible job shop scheduling problemProceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2598394.2602280(1449-1450)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2598394.2602280
Show More Cited By

Support vector machines training data selection using a genetic algorithm
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches

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Information & Contributors

Information

Published In

cover image Guide Proceedings

SSPR'12/SPR'12: Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition

November 2012

754 pages

ISBN:9783642341656

Editors:
Georgy Gimel'farb
Department of Computer Science, University of Auckland, Auckland, New Zealand
,
Edwin Hancock
Department of Computer Science, University of York, Deramore Lane, York, UK
,
Atsushi Imiya
Institute of Media and Information Technology, Chiba University, Chiba, Japan
,
Arjan Kuijper
Institute of Media and Information Technology, Technische Universität/Fraunhofer IGD, Darmstadt, Chiba, Germany
,
Mineichi Kudo
Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Chiba, Japan

Sponsors

Hokkaido University
IAPR: International Association for Pattern Recognition
Chiba University: Chiba University, Japan
Tohoku University: Tohoku University
HU: Hiroshima University

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 07 November 2012

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Cited By

Birzhandi PKim KYoun H(2022)Reduction of training data for support vector machine: a surveySoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-06787-526:8(3729-3742)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s00500-022-06787-5
Grzejszczak TKawulok MGaluszka A(2016)Hand landmarks detection and localization in color imagesMultimedia Tools and Applications10.1007/s11042-015-2934-575:23(16363-16387)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1007/s11042-015-2934-5
Cwiek MNalepa JArnold D(2014)A fast genetic algorithm for the flexible job shop scheduling problemProceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2598394.2602280(1449-1450)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2598394.2602280
Nalepa JArnold D(2014)Adaptive memetic algorithm for the vehicle routing problem with time windowsProceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2598394.2602273(1467-1468)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2598394.2602273
Nalepa JKawulok MArnold D(2014)A memetic algorithm to select training data for support vector machinesProceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2576768.2598370(573-580)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2576768.2598370
Kawulok MNalepa J(2014)Dynamically Adaptive Genetic Algorithm to Select Training Data for SVMsAdvances in Artificial Intelligence -- IBERAMIA 201410.1007/978-3-319-12027-0_20(242-254)Online publication date: 24-Nov-2014
https://dl.acm.org/doi/10.1007/978-3-319-12027-0_20

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