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 language | English |
|---|---|
| Title of host publication | Rough Sets and Knowledge Technology - 4th International Conference, RSKT 2009, Proceedings |
| Pages | 94-101 |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - 2009 Aug 27 |
| Event | 4th International Conference on Rough Sets and Knowledge Technology, RSKT 2009 - Gold Coast, QLD, Australia Duration: 2009 Jul 14 → 2009 Jul 16 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 5589 LNAI |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 4th International Conference on Rough Sets and Knowledge Technology, RSKT 2009 |
|---|---|
| Country | Australia |
| City | Gold Coast, QLD |
| Period | 09-07-14 → 09-07-16 |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
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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 proceeding › Conference contribution
TY - GEN
T1 - Improving rules quality generated by rough set theory for the diagnosis of students with lds through mixed samples clustering
AU - Wu, Tung-Kuang
AU - Huang, Shian-Chang
AU - Meng, Ying Ru
AU - Lin, Yu Chi
PY - 2009/8/27
Y1 - 2009/8/27
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=69049083611&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=69049083611&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-02962-2_12
DO - 10.1007/978-3-642-02962-2_12
M3 - Conference contribution
AN - SCOPUS:69049083611
SN - 3642029612
SN - 9783642029615
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 94
EP - 101
BT - Rough Sets and Knowledge Technology - 4th International Conference, RSKT 2009, Proceedings
ER -