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Authors: Jianhua Dai Huifeng Han Hu Hu Qinghua Hu Jinghong Zhang Wentao Wang
Publish Date: 2016/6/3
Volume: , Issue: , Pages: 392-402
Abstract
Rough set theory supplying an effective model for representation of uncertain knowledge has been widely used in knowledge engineering and data mining Especially rough set theory has been used as an attribute selection method with much success However current rough set approaches for attribute reduction are unsuitable for semisupervised learning as no enough labeled data can guarantee to calculate the dependency degree We propose a new attribute selection strategy based on rough sets called DualPOS It provides mutual function mechanism of multiattributes and generates the most consistent one as a candidate Experiments are carried out to test the performances of classification and clustering of the proposed algorithm The results show that DualPOS is valid for attribute selection in semisupervised learningThis work was partially supported by the National Natural Science Foundation of China No 61473259 No 61070074 No 60703038 the Zhejiang Provincial Natural Science Foundation No Y14F020118 the National Science Technology Support Program of China 2015BAK26B00 2015BAK26B02 and the PEIYANG Young Scholars Program of Tianjin University 2016XRX0001
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