Los Angeles, CA
March 31, 2009 to April 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.481
Credit scoring models have been widely studied in academic world and the business community. Many novel approaches such as artificial neural networks (ANNs), rough sets, or decision trees have been proposed to increase the accuracy of credit scoring models. the C4.5 is a learning algorithm which adopts local search strategy, it cannot obtain the best decision rules. On the other hand, the simulated annealing algorithm is a global optimized algorithm, it avoids the drawbacks of C4.5. This paper proposes a new credit scoring model based on decision tree and simulated annealing algorithm. The experimental results demonstrate that the proposed model is effective.
Yi Jiang, "Credit Scoring Model Based on the Decision Tree and the Simulated Annealing Algorithm", CSIE, 2009, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, pp. 18-22, doi:10.1109/CSIE.2009.481