By Topic

A Decision Tree Scoring Model Based on Genetic Algorithm and K-Means Algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Defu Zhang ; Dept. of Comput. Sci., Xiamen Univ., Xiamen ; Stephen C. H. Leung ; Zhimei Ye

Credit scoring has been regarded as a critical topic and studied extensively in the finance field. Many artificial intelligence techniques have been used to solve credit scoring. The paper is to build a classification model based on a decision tree by learning historical data. Clustering algorithm and genetic algorithm are combined to further improve the accuracy of this credit scoring model. The clustering algorithm aims at removing noise data, while the genetic algorithm is used to reduce the redundancy attribute of data. The computational results on the two real world benchmark data sets show that the presented hybrid model is efficient.

Published in:

Convergence and Hybrid Information Technology, 2008. ICCIT '08. Third International Conference on  (Volume:1 )

Date of Conference:

11-13 Nov. 2008