Affiliations: [a] School of Physics and Telecommunication Engineering, Yulin Normal University, Yulin, Guangxi, P.R. China
| [b] Key Laboratory of Complex System Optimization and Big Data Processing in Department of Guangxi Education, Center for Applied Mathematics of Guangxi, Yulin Normal University, Yulin, Guangxi, P.R. China
Correspondence:
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Corresponding author. Lijun Chen, Key Laboratory of Complex System Optimization and Big Data Processing in Department of Guangxi Education, Center for Applied Mathematics of Guangxi, Yulin Normal University, Yulin, Guangxi 537000, P.R. China. E-mail: [email protected].
Abstract: Incomplete real-valued data often misses some labels due to the high cost of labeling data. This paper investigates for partially labeled incomplete real-valued data and considers its application in semi-supervised attribute reduction. There are two decision information systems (DISs) in a partially labeled incomplete real-valued data DIS (p-IRVDIS): a labeled incomplete real-valued data DIS (l-IRVDIS) and a unlabeled incomplete real-valued data DIS (u-IRVDIS). The degree of importance on an attribute subset in a p-IRVDIS are defined using an indistinguishable relation and conditional information entropy. It is the weighted sum of l-IRVDIS and u-IRVDIS using the missing rate of label to measure p-IRVDIS uncertainty. Based on the degree of importance, an adaptive semi-supervised attribute reduction algorithm in a p-IRVDIS is proposed. This algorithm can automatically adapt to various missing rates of label. The experimental results on 8 datasets reveal that the proposed algorithm performs statistically better than some state-of-the-art algorithms.
Keywords: p-IRVDIS, the degree of importance, semi-supervised attribute reduction, indiscernibility relation, conditional information entropy
