Article

Feature Selection for Support Vector Machines by Means of Genetic Algorithms

Authors:

Holger Fröhlich,

Olivier Chapelle,

Bernhard SchölkopfAuthors Info & Claims

ICTAI '03: Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence

Page 142

Published: 03 November 2003 Publication History

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Abstract

The problem of feature selection is a difficult combinatorial task in Machine Learning and of high practical relevance, e.g. in bioinformatics. Genetic Algorithms (GAs) offer a natural way to solve this problem. In this paper we present a special Genetic Algorithm, which especially takes into account the existing bounds on the generalization error for Support Vector Machines (SVMs). This new approach is compared to the traditional method of performing cross-validation and to other existing algorithms for feature selection.

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Saleh ATalaat FLabib L(2019)A hybrid intrusion detection system (HIDS) based on prioritized k-nearest neighbors and optimized SVM classifiersArtificial Intelligence Review10.1007/s10462-017-9567-151:3(403-443)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1007/s10462-017-9567-1
Tokuhara FMiyahara TKuboyama TSuzuki YUchida T(2018)Aggregative context-aware fitness functions based on feature selection for evolutionary learning of characteristic graph patternsVietnam Journal of Computer Science10.1007/s40595-018-0118-85:3-4(229-239)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s40595-018-0118-8
Kuo RHuang SZulvia FLiao T(2018)Artificial bee colony-based support vector machines with feature selection and parameter optimization for rule extractionKnowledge and Information Systems10.1007/s10115-017-1083-855:1(253-274)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1007/s10115-017-1083-8
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Index Terms

Feature Selection for Support Vector Machines by Means of Genetic Algorithms
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction
  2. Machine learning
    1. Machine learning algorithms
      1. Feature selection
2. Theory of computation
  1. Randomness, geometry and discrete structures

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Published In

ICTAI '03: Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence

November 2003

ISBN:0769520383

Publisher

IEEE Computer Society

United States

Publication History

Published: 03 November 2003

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Saleh ATalaat FLabib L(2019)A hybrid intrusion detection system (HIDS) based on prioritized k-nearest neighbors and optimized SVM classifiersArtificial Intelligence Review10.1007/s10462-017-9567-151:3(403-443)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1007/s10462-017-9567-1
Tokuhara FMiyahara TKuboyama TSuzuki YUchida T(2018)Aggregative context-aware fitness functions based on feature selection for evolutionary learning of characteristic graph patternsVietnam Journal of Computer Science10.1007/s40595-018-0118-85:3-4(229-239)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s40595-018-0118-8
Kuo RHuang SZulvia FLiao T(2018)Artificial bee colony-based support vector machines with feature selection and parameter optimization for rule extractionKnowledge and Information Systems10.1007/s10115-017-1083-855:1(253-274)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1007/s10115-017-1083-8
Phan ANguyen MBui L(2017)Feature weighting and SVM parameters optimization based on genetic algorithms for classification problemsApplied Intelligence10.1007/s10489-016-0843-646:2(455-469)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1007/s10489-016-0843-6
Zorarpacı EÖzel S(2016)A hybrid approach of differential evolution and artificial bee colony for feature selectionExpert Systems with Applications: An International Journal10.1016/j.eswa.2016.06.00462:C(91-103)Online publication date: 15-Nov-2016
https://dl.acm.org/doi/10.1016/j.eswa.2016.06.004
Sarojini B(2015)An integrated approach of feature selection and parameter optimisation of kernel to enhance the performance of support vector machineInternational Journal of Communication Networks and Distributed Systems10.1504/IJCNDS.2015.07098215:2/3(265-278)Online publication date: 1-Aug-2015
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Motai YMa DDocef AYoshida H(2015)Smart Colonography for Distributed Medical Databases with Group Kernel Feature AnalysisACM Transactions on Intelligent Systems and Technology10.1145/26681366:4(1-24)Online publication date: 27-Jul-2015
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(2014)Comprehensive learning particle swarm optimization based memetic algorithm for model selection in short-term load forecasting using support vector regressionApplied Soft Computing10.1016/j.asoc.2014.09.00725:C(15-25)Online publication date: 1-Dec-2014
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Chen BChen LChen Y(2013)Efficient ant colony optimization for image feature selectionSignal Processing10.5555/2445637.244594293:6(1566-1576)Online publication date: 1-Jun-2013
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Wang ZShao YWu T(2013)A GA-based model selection for smooth twin parametric-margin support vector machinePattern Recognition10.1016/j.patcog.2013.01.02346:8(2267-2277)Online publication date: 1-Aug-2013
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