Using statistical models and case-based reasoning in claims prediction: experience from a real-world problem

摘要

Case-based reasoning (CBR) has been widely used in many real-world applications. In general, CBR systems propose their answers based on solutions attached with the most similar cases retrieved from their case bases. However, in our vehicle insurance domain where the dataset contains a large amount of inconsistencies, proposing solutions based only on the most similar cases results in unacceptable answers. In this article, we propose a hybrid-reasoning algorithm which employs a number of statistical models derived from analysis of the entire dataset as an alternative reasoning method. Results of our experiments have shown that the use of these models enable our experimental system to propose better solutions than answers proposed based only on the closest matched cases.