research-article

An efficient SVM-GA feature selection model for large healthcare databases

Authors:

Michael Blackmon,

Marsha DowellAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 1373 - 1380

https://doi.org/10.1145/1389095.1389362

Published: 12 July 2008 Publication History

Abstract

This paper presents an efficient hybrid feature selection model based on Support Vector Machine (SVM) and Genetic Algorithm (GA) for large healthcare databases. Even though SVM and GA are robust computational paradigms, the combined iterative nature of a SVM-GA hybrid system makes runtime costs infeasible when using large databases. This paper utilizes hierarchical clustering to reduce dataset size and SVM training time, multi-resolution parameter search for efficient SVM model selection, and chromosome caching to avoid redundant fitness evaluations. This approach significantly reduces runtime and improves classification performance.

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Cited By

Alkubabji MAldasht MAdi S(2018)One Class Genetic-Based Feature Selection for Classification in Large DatasetsBig Data, Cloud and Applications10.1007/978-3-319-96292-4_24(301-311)Online publication date: 14-Aug-2018
https://doi.org/10.1007/978-3-319-96292-4_24
Liu FLu LHuang G(2017)Sparse autoencoder based feature learning for unmanned aerial vehicle landforms image classification2017 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM)10.1109/ICCIS.2017.8274739(1-6)Online publication date: Nov-2017
https://doi.org/10.1109/ICCIS.2017.8274739
Gou HShi TYan LXiao J(2016)Gait and Posture Analysis Method Based on Genetic Algorithm and Support Vector Machines with Acceleration DataJournal of Robotics and Mechatronics10.20965/jrm.2016.p041828:3(418-424)Online publication date: 17-Jun-2016
https://doi.org/10.20965/jrm.2016.p0418
Show More Cited By

Index Terms

An efficient SVM-GA feature selection model for large healthcare databases
1. Computing methodologies
  1. Machine learning
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Markov decision processes

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Published In

cover image ACM Conferences

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Alkubabji MAldasht MAdi S(2018)One Class Genetic-Based Feature Selection for Classification in Large DatasetsBig Data, Cloud and Applications10.1007/978-3-319-96292-4_24(301-311)Online publication date: 14-Aug-2018
https://doi.org/10.1007/978-3-319-96292-4_24
Liu FLu LHuang G(2017)Sparse autoencoder based feature learning for unmanned aerial vehicle landforms image classification2017 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM)10.1109/ICCIS.2017.8274739(1-6)Online publication date: Nov-2017
https://doi.org/10.1109/ICCIS.2017.8274739
Gou HShi TYan LXiao J(2016)Gait and Posture Analysis Method Based on Genetic Algorithm and Support Vector Machines with Acceleration DataJournal of Robotics and Mechatronics10.20965/jrm.2016.p041828:3(418-424)Online publication date: 17-Jun-2016
https://doi.org/10.20965/jrm.2016.p0418
(2014)A biological continuum based approach for efficient clinical classificationJournal of Biomedical Informatics10.1016/j.jbi.2013.09.00247:C(28-38)Online publication date: 1-Feb-2014
https://dl.acm.org/doi/10.1016/j.jbi.2013.09.002
Du XCheng JSong J(2009)Improved Prediction of Protein Binding Sites from Sequences Using Genetic AlgorithmThe Protein Journal10.1007/s10930-009-9192-128:6(273-280)Online publication date: 24-Jul-2009
https://doi.org/10.1007/s10930-009-9192-1
Nunes ASilva APaiva A(2009)Detection of Masses in Mammographic Images Using Simpson's Diversity Index in Circular Regions and SVMProceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition10.1007/978-3-642-03070-3_41(540-553)Online publication date: 21-Jul-2009
https://dl.acm.org/doi/10.1007/978-3-642-03070-3_41

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