关键词: 数据挖掘, 代价敏感, 属性约简, 最优因子

Abstract: Cost-sensitive learning is one of the most challenging problems in the current stage of data mining and machine learning research. Attribute reduction is an important classical problem of data mining. Cost-sensitive attribute reduction problem is an extension of traditional attribute reduction problem, and has become a hot research object. This paper analyzes and compares current typical five cost-sensitive attribute reduction algorithms for nominal data, and summarizes the main features and shortcomings of five algorithms, thereby researchers can improve old algorithms or develop new effective ones. The summary can also be used to select data mining techniques for new applications.

Key words: data mining, cost sensitive, attribute reduction, optimal factor