Fuzzy-Rough Instance Selection Combined with Effective Classifiers in Credit Scoring

Abstract

The wrong clusters number or poor starting points of each cluster have negative influence on the classification accuracy in the hybrid classifier based credit scoring system. The paper represents a new hybrid classifier based on fuzzy-rough instance selection, which have the same ability as clustering algorithms, but it can eliminate isolated and inconsistent instances without the need of determining clusters number and starting points of each cluster. The unrepresentative instances that cause conflicts with other instances are completely determined by the fuzzy-rough positive region which is only related to intrinsic data structure of datasets. By removing unrepresentative instances, both the training data quality and classifier training time can be improved. To prevent eliminating more instances than strictly necessary, the k-nearest neighbor algorithm is adopted to check the eliminated instances, and the instance whose predicted class is the same with predefined class is added back. SVM classifier with three different kernel functions are applied to the reduced dataset. The experimental results show that the proposed hybrid classifier has better classification accuracy on two real world datasets.