Cost-Sensitive LVQ for Bankruptcy Prediction: An Empirical Study

摘要：

Cost-sensitive learning is of critical importance in many domains including bankruptcy prediction where the costs of different errors are unequal. Most existing classification methods aim to minimize overall error based on the assumption that the costs are equal. This paper presents three costsensitive learning vector quantization (LVQ) approaches to incorporate cost matrix in classification. Experimental results on real-world data indicate the proposed approaches are effective alternatives for bankruptcy prediction in costsensitive situations.

作者: Ning Chen Armando Vieira Joao Duarte

作者单位: GECAD (Knowledge Engineering and Decision Support Group) Instituto Superior de Engenharia do Porto, Instituto Politecnico do Porto Rua Dr. Antonio Bernardino de Almeida, 431, 4200-072, Porto, Portugal

会议类型: 国际会议

会议名称: 2009 2nd IEEE International Conference on Computer Science and Information Technology(第二届计算机科学与信息技术国际会议 ICCSIT2009)

会议地点: 北京

会议语种:英文

页码: 2772-2776

在线出版日期: 2009-08-08（万方平台首次上网日期，不代表论文的发表时间）