Improving the performance of the support vector machine in insurance risk classification: A comparitive study

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.