Credit Scoring Analysis Using B-Cell Algorithm and K-Nearest Neighbor Classifiers

摘要：

　　This paper applies B-Cell algorithm (BCA) for credit scoring analysis problems.The proposed BCA-based method is combined with k-nearest neighbor (kNN) classifiers.In the algorithm, BCA is introduced to select the optimal feature subsets and kNNs are used to classify the investors in different groups representing different levels of credit in the classification phase.Experiments employing the benchmark data sets from UCI databases will be used to measure the performance of the algorithm.Its comparison with genetic algorithm, particle swarm optimization and ant colony optimization will be shown.

关键词： Classification Credit Scoring K-Nearest Neighbor Classifiers B-Cell Algorithm

作者: Cheng-An Li

作者单位: College of Economics and Commerce South China University of Technology, Guangzhou, 510006, P.R.China

会议类型: 国际会议

会议名称: 4th international Conference,ICSI2013(第4届群体智能国际会议)

会议地点: 哈尔滨

会议语种:英文

页码: 191-199

在线出版日期: 2013-06-12（万方平台首次上网日期，不代表论文的发表时间）