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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationRough Sets and Knowledge Technology - 4th International Conference, RSKT 2009, Proceedings
Pages94-101
Number of pages8
DOIs
Publication statusPublished - 2009 Aug 27
Event4th International Conference on Rough Sets and Knowledge Technology, RSKT 2009 - Gold Coast, QLD, Australia
Duration: 2009 Jul 142009 Jul 16

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5589 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Conference on Rough Sets and Knowledge Technology, RSKT 2009
CountryAustralia
CityGold Coast, QLD
Period09-07-1409-07-16

Fingerprint

Rough set theory
Disability
Rough Set Theory
Clustering
Students
Decision trees
Clustering algorithms
Decision tree
Clustering Algorithm
Preprocessing
Learning
Processing
Prediction
Term
Experiments
Experiment

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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. In Rough Sets and Knowledge Technology - 4th International Conference, RSKT 2009, Proceedings (pp. 94-101). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5589 LNAI). https://doi.org/10.1007/978-3-642-02962-2_12
Wu, Tung-Kuang ; Huang, Shian-Chang ; Meng, Ying Ru ; Lin, Yu Chi. / Improving rules quality generated by rough set theory for the diagnosis of students with lds through mixed samples clustering. Rough Sets and Knowledge Technology - 4th International Conference, RSKT 2009, Proceedings. 2009. pp. 94-101 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Wu, T-K, Huang, S-C, 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 Rough Sets and Knowledge Technology - 4th International Conference, RSKT 2009, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5589 LNAI, pp. 94-101, 4th International Conference on Rough Sets and Knowledge Technology, RSKT 2009, Gold Coast, QLD, Australia, 09-07-14. https://doi.org/10.1007/978-3-642-02962-2_12

Improving rules quality generated by rough set theory for the diagnosis of students with lds through mixed samples clustering. / Wu, Tung-Kuang; Huang, Shian-Chang; Meng, Ying Ru; Lin, Yu Chi.

Rough Sets and Knowledge Technology - 4th International Conference, RSKT 2009, Proceedings. 2009. p. 94-101 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5589 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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