Long Term Bank Failure Prediction using Fuzzy Refinement-based Transductive Transfer Learning

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
2011 IEEE International Conference on Fuzzy Systems (FUZZ), 2011, pp. 2676 - 2683
Issue Date:
2011-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2011004475OK.pdf293.66 kB
Adobe PDF
Machine learning algorithms, which have been considered as robust methods in different computational fields, assume that the training and test data are drawn from the same distribution. This assumption may be violated in many real world applications like bank failure prediction because training and test data may come from different time periods or domains. An efficient novel algorithm known as Fuzzy Refinement (FR) is proposed in this paper to solve this problem and improve the performance. The algorithm utilizes the fuzzy system and similarity concept to modify the instances' labels in target domain which was initially predicted by shift-unaware Fuzzy Neural Network (FNN) proposed by [1]. The experiments are performed using bank failure financial data of United States to evaluate the algorithm performance. The results address a significant improvement in the predictive accuracy of FNN due to applying the proposed algorithm.
Please use this identifier to cite or link to this item: