Paper Info
Title
Knowledge change rate-based attribute importance measure and its performance analysis.
Abstract
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 BCKCRAIM. We discuss theoretical construction strategies and structural features followed by remarks on constructing BCKCRAIM. Finally, experimental results for several examples and UCI data sets show the connections and differences between BCKCRAIM 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, BCKCRAIM has wide application in such approaches as attributes reduction, feature extraction, information fusion, and expert systems.
Year
DOI
Venue
2017
10.1016/j.knosys.2016.12.002
Knowl.-Based Syst.
Keywords
Field
DocType
Decision system,Fuzzy measure,Knowledge acquisition,Entropy,Important measure
Data mining,Interpretability,Data set,Computer science,Expert system,Variable and attribute,Fuzzy logic,Feature extraction,Artificial intelligence,Information fusion,Knowledge acquisition,Machine learning
Journal
Volume
Issue
ISSN
119
C
0950-7051
Citations 
PageRank 
References 
2
0.36
21
Authors
3
Name
Order
Citations
PageRank
Chenxia Jin110113.20
Fachao Li215722.30
Qihui Hu320.36