摘要： 为提高信用评估的预测精度，提出一种基于装袋的基因表达式编程(GEP)多分类器集成算法。该算法采用Bagging方法将GEP产生的多个差异基分类器进行集成。在德国信用数据库真实数据集上的实验及性能分析表明，该算法较SVM算法的预测精度提高约2.7%；较KNN(K=17)算法的预测精度提高约7.93%；较单GEP分类算法的预测精度提高约1.1%。
关键词:
装袋技术,
基因表达式编程,
信用评估,
分类器集成,
预测精度
Abstract: To improve the prediction precision in credit evaluation, a novel classifier ensemble algorithm based on Gene Expressiong Programming(GEP) with Bagging, called BGEP-CREDIT, is proposed. The algorithm uses Bagging to combine the several GEP classifiers generated from GEP. Experiments and performance analysis on Germany credit database are given. The results show that compared with SVM algorithm, KNN(K=17) algorithm and a single GEP classifier, the prediction precision is increased by 2.7%, 7.93% and 1.1% respectively by using BGEP-CREDIT.
Key words:
Bagging technology,
Gene Expressiong Programming(GEP),
credit evaluation,
classifier integration,
prediction accuracy
中图分类号:
刘凯英, 吴江, 李太勇. 基于装袋GEP分类器集成的信用评估[J]. 计算机工程, 2011, 37(8): 213-215.
LIU Kai-Yang, TUN Jiang, LI Ta-Yong. Credit Evaluation Based on Bagging GEP Classifier Integration[J]. Computer Engineering, 2011, 37(8): 213-215.