Knowledge change rate-based attribute importance measure and its performance analysis

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• 作者：Chenxia Jin ^{jinchenxia2005@126.com} ; Fachao Li ; ^{lifachao@tsinghua.org.cn} ; Qihui Hu ^{15832170220@163.com}
• 关键词：Decision system ; Fuzzy measure ; Knowledge acquisition ; Entropy ; Important measure
• 刊名：Knowledge-Based Systems
• 出版年：2017
• 出版时间：1 March 2017
• 年：2017
• 卷：119
• 期：Complete
• 页码：59-67
• 全文大小：617 K
• 卷排序：119

文摘

Attribute importance measure is important in such approaches as data system reduction and, multi-attribute decisions. In this paper, we present knowledge change rate-based attribute importance measures with structural features of fuzzy measure, abbreviated as BCKCR–AIM. We discuss theoretical construction strategies and structural features followed by remarks on constructing BCKCR–AIM. Finally, experimental results for several examples and UCI data sets show the connections and differences between BCKCR–AIM and other attribute importance measures. The advantage of our measure is that it uses attributes set changes to describe knowledge change and associated features between lower and upper approximations of decision classes and knowledge to reflect attribute importance. Our measure can improve feasibility and interpretability; therefore, BCKCR–AIM has wide application in such approaches as attributes reduction, feature extraction, information fusion, and expert systems.