摘要
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。
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.
出处
《计算机科学与探索》
CSCD
2014年第9期1137-1145,共9页
Journal of Frontiers of Computer Science and Technology
基金
国家自然科学基金面上项目
福建省教育厅科技重点项目
漳州市自然科学基金~~
关键词
数据挖掘
代价敏感
属性约简
最优因子
data mining
cost sensitive
attribute reduction
optimal factor
