CAJ | 학술논문

代价敏感学习是数据挖掘研究领域最具有挑战性的问题之一。属性约简是数据挖掘中重要的经典问题。代价敏感属性约简问题是对经典属性约简问题的自然扩展，已经逐渐成为研究的热点。对当前具有代表性的5种处理名词性数据的代价敏感属性约简算法进行了分析和比较，总结了每种算法的各方面特性及不足之处，从而便于研究者对已有算法进行改进，并且进一步提出具有更好性能的新的约简算法，方便用户对算法的选择和使用。
대개민감학습시수거알굴연구영역최구유도전성적문제지일。속성약간시수거알굴중중요적경전문제。대개민감속성약간문제시대경전속성약간문제적자연확전，이경축점성위연구적열점。대당전구유대표성적5충처리명사성수거적대개민감속성약간산법진행료분석화비교，총결료매충산법적각방면특성급불족지처，종이편우연구자대이유산법진행개진，병차진일보제출구유경호성능적신적약간산법，방편용호대산법적선택화사용。
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 attri-bute 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 nomi-nal 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.