Abstract There are a number of unsupervised feature selection methods proposed for single-valued information systems, but little research focuses on unsupervised feature selection of interval-valued information systems.In this paper, a fuzzy dominance relation is proposed for interval ordered information systems. Then, fuzzy rank information entropy and fuzzy rank mutual information are extended to evaluate the importance of features. Consequently, an unsupervised feature selection method is designed based on an unsupervised maximum information and minimum redundancy(UmImR) criterion. In the UmImR criterion, the amount of information and redundancy are taken into account. Experimental results demonstrate the effectiveness of the proposed method.
Fund:Supported by National Natural Science Foundation of China(No.61473259,61070074), National Science & Technology Support Program of China(No.2015BAK26B00,2015BAK26B02), BEIYANG Young Scholars Program of Tianjin University(No.2016XRX-0001)
About author:: (YAN Yuejun, born in 1992, master student. Her research interests include rough set theory.) (DAI Jianhua(Corresponding author), born in 1977, Ph. D., professor. His research interests include artificial intelligence, machine learning, data mining and soft computing.)
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2017, Vol. 30 
