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

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.

► Preference theory functions could be used for similarity measurements between cases. ► Preference functions can improve the performance of the traditional CBR system. ► For setting the parameters of preference functions, it is used a genetic algorithm. ► Genetic algorithm optimizes the features’ importance in proposed CBR model.