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

Authors:
Defu Zhang;Stephen C. H. Leung;Zhimei Ye
Affiliations:
-;-;-
Venue:
ICCIT '08 Proceedings of the 2008 Third International Conference on Convergence and Hybrid Information Technology - Volume 01
Year:
2008

Citing 0
Cited 3

Bankruptcy forecasting: A hybrid approach using Fuzzy c-means clustering and Multivariate Adaptive Regression Splines (MARS)

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Two credit scoring models based on dual strategy ensemble trees

Knowledge-Based Systems
A hybrid device for the solution of sampling bias problems in the forecasting of firms' bankruptcy

Expert Systems with Applications: An International Journal

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Abstract

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.