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DualPOS: A Semi-supervised Attribute Selection Approach for Symbolic Data Based on Rough Set Theory

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Part of the book series:Lecture Notes in Computer Science ((LNISA,volume 9659))

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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 semi-supervised 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 multi-attributes, 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 semi-supervised learning.

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Acknowledgements

This 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 (2016XRX-0001).

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Authors and Affiliations

  1. School of Computer Science and Technology, Tianjin University, Tianjin, 300350, China

    Jianhua Dai & Qinghua Hu

  2. College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China

    Jianhua Dai, Huifeng Han, Hu Hu, Jinghong Zhang & Wentao Wang

Authors
  1. Jianhua Dai

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  2. Huifeng Han

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  3. Hu Hu

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  4. Qinghua Hu

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  5. Jinghong Zhang

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  6. Wentao Wang

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Corresponding author

Correspondence toJianhua Dai.

Editor information

Editors and Affiliations

  1. Peking University , Beijing, China

    Bin Cui

  2. The George Washington University , Washington, D.C., USA

    Nan Zhang

  3. Hong Kong Baptist University, Kowloon Tong, Hong Kong, China

    Jianliang Xu

  4. University of Texas Rio Grande Valley, Edinburg, Texas, USA

    Xiang Lian

  5. Jiangxi University of Finance and Economics, Nanchang, Jiangxi, China

    Dexi Liu

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© 2016 Springer International Publishing Switzerland

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Dai, J., Han, H., Hu, H., Hu, Q., Zhang, J., Wang, W. (2016). DualPOS: A Semi-supervised Attribute Selection Approach for Symbolic Data Based on Rough Set Theory. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9659. Springer, Cham. https://doi.org/10.1007/978-3-319-39958-4_31

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