Default Probability Prediction Of Credit Applicants Using A New Fuzzy Knn Method With Optimal Weights

Abstract

Credit scoring has become a very important issue due to the recent growth of the credit industry. As the first objective, this chapter provides an academic database of literature between and proposes a classification scheme to classify the articles. The second objective of this chapter is to suggest the employing of the Optimally Weighted Fuzzy K-Nearest Neighbor (OWFKNN) algorithm for credit scoring. To show the performance of this method, two real world datasets from UCI database are used. In classification task, the empirical results demonstrate that the OWFKNN outperforms the conventional KNN and fuzzy KNN methods and also other methods. In the predictive accuracy of probability of default, the OWFKNN also show the best performance among the other methods. The results in this chapter suggest that the OWFKNN approach is mostly effective in estimating default probabilities and is a promising method to the fields of classification.

Publication Date

6-4-2018

Publication Title

Intelligent Systems: Concepts, Methodologies, Tools, and Applications

Number of Pages

1838-1874

Document Type

Article; Book Chapter

Personal Identifier

scopus

DOI Link

https://doi.org/10.4018/978-1-5225-5643-5.ch082

Socpus ID

85059707023 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85059707023

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