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Improving Rules Quality Generated by Rough Set Theory for the Diagnosis of Students with LDs through Mixed Samples Clustering

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Rough Sets and Knowledge Technology (RSKT 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5589))

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Abstract

Due to the implicit characteristics of learning disabilities (LDs), the identification or diagnosis of students with LDs has long been a difficult issue. In this study, we apply rough set theory (RST), which may produce meaningful explanations or rules, to the LD identification application. We also propose to mix samples collected from sources with different LD diagnosis procedure and criteria. By pre-processing these mixed samples with some simple and readily available clustering algorithms, we are able to improve the quality of rules generated by RST. Our experiments also indicate that RST performs better in term of prediction certainty than other rule-based algorithms such as decision tree and ripper algorithms. Overall, we believe that RST may have the potential in playing an essential role in the field of LD diagnosis.

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Author information

Authors and Affiliations

  1. Dept. of Information Management, National Changhua University of Education, Changhua, Taiwan

    Tung-Kuang Wu & Yu-Chi Lin

  2. Dept. of Business Administration, National Changhua University of Education, Changhua, Taiwan

    Shian-Chang Huang

  3. Dept. of Special Education, National HsinChu University of Education, HsinChu, Taiwan

    Ying-Ru Meng

Authors
  1. Tung-Kuang Wu
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  2. Shian-Chang Huang
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  3. Ying-Ru Meng
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  4. Yu-Chi Lin
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Editor information

Editors and Affiliations

  1. Faculty of Engineering and Surveying and Engineering, University of Southern Queensland, QLD 4350, Australia

    Peng Wen

  2. School of Information Technology, Queensland University of Technology, QLD 4001, Brisbane, Australia

    Yuefeng Li

  3. Institute of Mathematics, Warsaw University of Technology, Koszykowa 86, 02008, Warsaw, Poland

    Lech Polkowski

  4. Department of Computer Science, University of Regina, S4S 0A2, Regina, Saskatchewan, Canada

    Yiyu Yao

  5. Faculty of Medicine, Department of Medical Informatics, Shimane University, 89-1 Enya-cho, Izumo, 693-8501, Shimane, Japan

    Shusaku Tsumoto

  6. Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China

    Guoyin Wang

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© 2009 Springer-Verlag Berlin Heidelberg

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Wu, TK., Huang, SC., Meng, YR., Lin, YC. (2009). Improving Rules Quality Generated by Rough Set Theory for the Diagnosis of Students with LDs through Mixed Samples Clustering. In: Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S., Wang, G. (eds) Rough Sets and Knowledge Technology. RSKT 2009. Lecture Notes in Computer Science(), vol 5589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02962-2_12

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  • DOI: https://doi.org/10.1007/978-3-642-02962-2_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02961-5

  • Online ISBN: 978-3-642-02962-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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