The construction of an individual credit risk assessment method: based on the combination algorithms

Authors:
Jiajun Li;Liping Qin;Jia Zhao
Affiliations:
Economics Research Center, Northwestern Polytechnical University, Xi'an, Shaanxi, P.R. China;Economics Research Center, Northwestern Polytechnical University, Xi'an, Shaanxi, P.R. China;Economics Research Center, Northwestern Polytechnical University, Xi'an, Shaanxi, P.R. China
Venue:
ICICA'10 Proceedings of the First international conference on Information computing and applications
Year:
2010

Citing 11
Cited 0

The Strength of Weak Learnability

Machine Learning
Original Contribution: Stacked generalization

Neural Networks
Bagging predictors

Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting

Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
CURE: an efficient clustering algorithm for large databases

SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
On Combining Classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants

Machine Learning
Boosting and other ensemble methods

Neural Computation
Neural nets versus conventional techniques in credit scoring in Egyptian banking

Expert Systems with Applications: An International Journal
Multiple classifier application to credit risk assessment

Expert Systems with Applications: An International Journal
Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

As the rapid growth of personal credit business, we have always been seeking to establish an effective risk assessment model to achieve low costs and better accuracy of decision-making. Over the past few years, the so-called combined algorithms have appeared in many fields, but they are always useless in the field of individual credit risk assessment. So we constructed a practical method based on combined algorithms, and we tested it empirically. The result shows that the application of the method can achieve better accuracy than the BP neural network.