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Authors: Mlungisi Duma 1 ; Bhekisipho Twala 1 ; Tshilidzi Marwala 1 and Fulufhelo V. Nelwamondo 2

Affiliations: 1 University of Johannesburg APK, South Africa ; 2 Council for Scientific and Industrial Research (CSIR), South Africa

ISBN: 978-989-8425-84-3

Keyword(s): Support vector machine, Principal component analysis, Genetic algorithms, Artificial neural network, Autoassociative network, Missing data.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer-Supported Education ; Data Manipulation ; Domain Applications and Case Studies ; Enterprise Information Systems ; Fuzzy Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Industrial, Financial and Medical Applications ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: The support vector machine is a classification technique used in linear and non- linear complex problems. It was shown that the performance of the technique decreases significantly in the presence of escalating missing data in the insurance domain. Furthermore the resilience of the technique when the quality of the data deteriorates is weak. When dealing with missing data, the support vector machine uses the mean-mode strategy to replace missing values. In this paper, we propose the use of the autoassociative network and the genetic algorithm as alternative strategies to help improve the classification performance as well as increase the resilience of the technique. A comparative study is conducted to see which of the techniques helps the support vector machine improve in performance and sustain resilience. The training data with completely observable data is used to construct the support vector machine and testing data with missing values is used to measuring the accuracy. The result s show that both models help increase resilience with the autoassociative network showing better overall performance improvement. (More)

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Paper citation in several formats:
Duma, M.; Twala, B.; Marwala, T. and V. Nelwamondo, F. (2011). IMPROVING THE PERFORMANCE OF THE SUPPORT VECTOR MACHINE IN INSURANCE RISK CLASSIFICATION - A Comparitive Study.In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 340-346. DOI: 10.5220/0003673803400346

@conference{ncta11,
author={Mlungisi Duma. and Bhekisipho Twala. and Tshilidzi Marwala. and Fulufhelo V. Nelwamondo.},
title={IMPROVING THE PERFORMANCE OF THE SUPPORT VECTOR MACHINE IN INSURANCE RISK CLASSIFICATION - A Comparitive Study},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)},
year={2011},
pages={340-346},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003673803400346},
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 - IMPROVING THE PERFORMANCE OF THE SUPPORT VECTOR MACHINE IN INSURANCE RISK CLASSIFICATION - A Comparitive Study
SN - 978-989-8425-84-3
AU - Duma, M.
AU - Twala, B.
AU - Marwala, T.
AU - V. Nelwamondo, F.
PY - 2011
SP - 340
EP - 346
DO - 10.5220/0003673803400346

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