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Authors: Kenji Nishida 1 ; Jun Fujiki 1 and Takio Kurita 2

Affiliations: 1 National Institute of Advanced Industrial Science and Technology (AIST), Japan ; 2 Hiroshima University, Japan

ISBN: 978-989-8425-84-3

Keyword(s): Ensemble learning, Bagging, Boosting, Generalization performance, Support vector machine.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Support Vector Machines and Applications ; Theory and Methods

Abstract: In this paper, the Ensemble Random-Subset SVM algorithm is proposed. In a random-subset SVM, multiple SVMs are used, and each SVM is considered a weak classifier; a subset of training samples is randomly selected for each weak classifier with randomly set parameters, and the SVMs with optimal weights are combined for classification. A linear SVM is adopted to determine the optimal kernel weights; therefore, an ensemble random-subset SVMis based on a hierarchical SVMmodel. An ensemble random-subset SVM outperforms a single SVMeven when using a small number of samples (10 or 100 samples out of 20,000 training samples for each weak classifier); in contrast, a single SVM requires more than 4,000 support vectors.

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Paper citation in several formats:
Nishida, K.; Fujiki, J. and Kurita, T. (2011). ENSEMBLE RANDOM-SUBSET SVM.In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 334-339. DOI: 10.5220/0003668903340339

@conference{ncta11,
author={Kenji Nishida. and Jun Fujiki. and Takio Kurita.},
title={ENSEMBLE RANDOM-SUBSET SVM},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)},
year={2011},
pages={334-339},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003668903340339},
isbn={978-989-8425-84-3},
}

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)
TI - ENSEMBLE RANDOM-SUBSET SVM
SN - 978-989-8425-84-3
AU - Nishida, K.
AU - Fujiki, J.
AU - Kurita, T.
PY - 2011
SP - 334
EP - 339
DO - 10.5220/0003668903340339

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