Paper Info
Title
Technology credit scoring model with fuzzy logistic regression.
Abstract
We propose a technology credit scoring model based on fuzzy logistic regression.Fuzzy predictor, fuzzy binary responses with crisp coefficients are considered.Fuzzy least square method is used to estimate parameters.The performance of proposed fuzzy logistic regression model is improved compared to the logistic regression. Technology credit scoring models have been used to screen loan applicant firms based on their technology. Typically a logistic regression model is employed to relate the probability of a loan default of the firms with several evaluation attributes associated with technology. However, these attributes are evaluated in linguistic expressions represented by fuzzy number. Besides, the possibility of loan default can be described in verbal terms as well. To handle these fuzzy input and output data, we proposed a fuzzy credit scoring model that can be applied to predict the default possibility of loan for a firm that is approved based on its technology. The method of fuzzy logistic regression as an appropriate prediction approach for credit scoring with fuzzy input and output was presented in this study. The performance of the model is improved compared to that of typical logistic regression. This study is expected to contribute to practical utilization of the technology credit scoring with linguistic evaluation attributes.
Year
DOI
Venue
2016
10.1016/j.asoc.2016.02.025
Appl. Soft Comput.
Keywords
Field
DocType
Technology credit scoring,Fuzzy prediction model,Fuzzy logistic regression
Data mining,Expression (mathematics),Input/output,Artificial intelligence,Fuzzy number,Logistic regression,Loan,Multinomial logistic regression,Fuzzy logic,Default,Statistics,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
43
C
1568-4946
Citations 
PageRank 
References 
10
0.53
29
Authors
3
Name
Order
Citations
PageRank
So Young Sohn162667.41
Dong Ha Kim2405.15
Jin Hee Yoon37710.77