A case-based reasoning model that uses preference theory functions for credit scoring

摘要

We propose a case-based reasoning (CBR) model that uses preference theory functions for similarity measurements between cases. As it is hard to select the right preference function for every feature and set the appropriate parameters, a genetic algorithm is used for choosing the right preference functions, or more precisely, for setting the parameters of each preference function, as to set attribute weights. The proposed model is compared to the well-known k-nearest neighbour (k-NN) model based on the Euclidean distance measure. It has been evaluated on three different benchmark datasets, while its accuracy has been measured with 10-fold cross-validation test. The experimental results show that the proposed approach can, in some cases, outperform the traditional k-NN classifier.