TY - GEN
T1 - IMPROVING THE PERFORMANCE OF THE SUPPORT VECTOR MACHINE IN INSURANCE RISK CLASSIFICATION A Comparitive Study
AU - Duma, Mlungisi
AU - Twala, Bhekisipho
AU - Marwala, Tshilidzi
AU - Nelwamondo, Fulufhelo V.
N1 - Publisher Copyright:
© 2011 SCITEPRESS (Science and Technology Publications, Lda.
PY - 2011
Y1 - 2011
N2 - 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 results show that both models help increase resilience with the autoassociative network showing better overall performance improvement.
AB - 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 results show that both models help increase resilience with the autoassociative network showing better overall performance improvement.
KW - Artificial neural network
KW - Autoassociative network
KW - Genetic algorithms
KW - Missing data
KW - Principal component analysis
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=105002036832&partnerID=8YFLogxK
U2 - 10.5220/0003673803400346
DO - 10.5220/0003673803400346
M3 - Conference contribution
AN - SCOPUS:105002036832
SN - 9789898425836
T3 - International Joint Conference on Computational Intelligence
SP - 340
EP - 346
BT - In Proceedings of the International Conference on Evolutionary Computation Theory and Applications, ECTA 2011
A2 - Rosa, Agostinho
PB - Science and Technology Publications, Lda
T2 - International Conference on Evolutionary Computation Theory and Applications, ECTA 2011
Y2 - 24 October 2011 through 26 October 2011
ER -