Title :
A dynamic self-adoptive genetic algorithm for personal credit risk assessment
Author :
Zhong, Xing ; Kou, Gang ; Peng, Yi
Author_Institution :
Sch. of Manage. & Econ., Univ. of Electron. Sci. & Technol. of China, Chengdu, China
Abstract :
Simple genetic algorithm has many defects, such as premature and slow speed of convergence. This paper researches the frame and performance of four combination algorithms based on dynamic self-adaptive genetic algorithm (DSGA-SVM, DSGA-Logistic, DSGA-C4.5, DSGA-BPNN). In order to classify the customers into two groups representing low and high credit risk, the proposed algorithms are tested using three countries´ personal credit data download from the website of UCI machine learning. Through the comparison of the algorithms proposed above we can verify the performance of DSGA-based algorithms and check out the most suitable algorithms to combine with DSGA.
Keywords :
Classification algorithms; Classification tree analysis; Genetic algorithms; Heuristic algorithms; Machine learning algorithms; Neural networks; Risk analysis; Risk management; Technology management; Testing; combination models; credit risk; dynamic self-adaptive; genetic algorithm;
Conference_Titel :
Information Sciences and Interaction Sciences (ICIS), 2010 3rd International Conference on
Conference_Location :
Chengdu, China
Print_ISBN :
978-1-4244-7384-7
Electronic_ISBN :
978-1-4244-7386-1
DOI :
10.1109/ICICIS.2010.5534692
