Cost Sensitive Classification in Data Mining

摘要：

Cost-sensitive classification is one of mainstream research topics in data mining and machine learning that induces models from data with unbalance class distributions and impacts by quantifying and tackling the unbalance. Rooted in diagnosis data analysis applications, there are great many techniques developed for cost-sensitive learning. They are mainly focused on minimizing the total cost of misclassification costs, test costs, or other types of cost, or a combination among these costs. This paper introduces the up-to-date prevailing costsensitive learning methods and presents some research topics by outlining our two new results: lazylearning and semi-learning strategies for costsensitive classifiers.

关键词： Cost sensitive learning misclassification cost test cost

作者: Zhenxing Qin Chengqi Zhang Tao Wang Shichao Zhang

作者单位: Faculty of Information Technology University of Technology Sydney PO Box 123 Broadway Sydney NSW 2007 Australia

会议类型: 国际会议

会议名称: 6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)

会议地点: 重庆

会议语种:英文

页码: 1-11

在线出版日期: 2010-11-19（万方平台首次上网日期，不代表论文的发表时间）