Improving rules quality generated by rough set theory for the diagnosis of students with lds through mixed samples clustering

Tung Kuang Wu, Shian Chang Huang, Ying Ru Meng, Yu Chi Lin

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題Rough Sets and Knowledge Technology - 4th International Conference, RSKT 2009, Proceedings
頁面94-101
頁數8
DOIs
出版狀態Published - 2009 八月 27
事件4th International Conference on Rough Sets and Knowledge Technology, RSKT 2009 - Gold Coast, QLD, Australia
持續時間: 2009 七月 142009 七月 16

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
5589 LNAI
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Other

Other4th International Conference on Rough Sets and Knowledge Technology, RSKT 2009
國家Australia
城市Gold Coast, QLD
期間09-07-1409-07-16

    指紋

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

引用此

Wu, T. K., Huang, S. C., Meng, Y. R., & Lin, Y. C. (2009). Improving rules quality generated by rough set theory for the diagnosis of students with lds through mixed samples clustering. 於 Rough Sets and Knowledge Technology - 4th International Conference, RSKT 2009, Proceedings (頁 94-101). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 5589 LNAI). https://doi.org/10.1007/978-3-642-02962-2_12