Comparing the knowledge quality in rough classifier and decision tree classifier
This paper presents a comparative study of two rule based classifier; rough set (Rc) and decision tree (DTc).Both techniques apply different approach to perform classification but produce same structure of output with comparable result. Theoretically, different classifiers will generate different se...
| Main Authors: | , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2008
|
| Subjects: | |
| Online Access: | https://repo.uum.edu.my/id/eprint/14121/1/04631700.pdf |
| _version_ | 1825803306262331392 |
|---|---|
| author | Mohamad Mohsin, Mohamad Farhan Abd Wahab, Mohd Helmy |
| author_facet | Mohamad Mohsin, Mohamad Farhan Abd Wahab, Mohd Helmy |
| author_sort | Mohamad Mohsin, Mohamad Farhan |
| collection | UUM |
| description | This paper presents a comparative study of two rule based classifier; rough set (Rc) and decision tree (DTc).Both techniques apply different approach to perform classification but produce same structure of output with comparable result. Theoretically, different classifiers will generate different sets of rules via knowledge even though they are implemented to the same classification problem.Hence, the aim of this paper is to investigate the quality of knowledge produced by Rc and DTc when similar problems are presented to them.In this case, four important performance metrics are used as comparison, the accuracy of classification, rules quantity, rules length and rules coverage.Five dataset from UCI Machine Learning are chosen and then mined using Rc toolkit namely ROSETTA while C4.5 algorithm in WEKA application is chosen as DTc rule generator. The experimental result shows that Rc and DTc own capability to generate quality knowledge since most of the results are comparable. Rc outperform as an accurate classifier, produce shorter and simpler rule with higher coverage. Meanwhile, DTc obviously generates fewer numbers of rules with significant difference. |
| first_indexed | 2024-07-04T05:54:49Z |
| format | Conference or Workshop Item |
| id | uum-14121 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T05:54:49Z |
| publishDate | 2008 |
| record_format | eprints |
| spelling | uum-141212015-05-17T03:26:42Z https://repo.uum.edu.my/id/eprint/14121/ Comparing the knowledge quality in rough classifier and decision tree classifier Mohamad Mohsin, Mohamad Farhan Abd Wahab, Mohd Helmy QA76 Computer software This paper presents a comparative study of two rule based classifier; rough set (Rc) and decision tree (DTc).Both techniques apply different approach to perform classification but produce same structure of output with comparable result. Theoretically, different classifiers will generate different sets of rules via knowledge even though they are implemented to the same classification problem.Hence, the aim of this paper is to investigate the quality of knowledge produced by Rc and DTc when similar problems are presented to them.In this case, four important performance metrics are used as comparison, the accuracy of classification, rules quantity, rules length and rules coverage.Five dataset from UCI Machine Learning are chosen and then mined using Rc toolkit namely ROSETTA while C4.5 algorithm in WEKA application is chosen as DTc rule generator. The experimental result shows that Rc and DTc own capability to generate quality knowledge since most of the results are comparable. Rc outperform as an accurate classifier, produce shorter and simpler rule with higher coverage. Meanwhile, DTc obviously generates fewer numbers of rules with significant difference. 2008 Conference or Workshop Item PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/14121/1/04631700.pdf Mohamad Mohsin, Mohamad Farhan and Abd Wahab, Mohd Helmy (2008) Comparing the knowledge quality in rough classifier and decision tree classifier. In: Information Technology on International Symposium 2008 (ITSim 2008), 26-28 Aug. 2008, Kuala Lumpur, Malaysia. http://doi.org/10.1109/ITSIM.2008.4631700 doi:10.1109/ITSIM.2008.4631700 doi:10.1109/ITSIM.2008.4631700 |
| spellingShingle | QA76 Computer software Mohamad Mohsin, Mohamad Farhan Abd Wahab, Mohd Helmy Comparing the knowledge quality in rough classifier and decision tree classifier |
| title | Comparing the knowledge quality in rough classifier and decision tree classifier |
| title_full | Comparing the knowledge quality in rough classifier and decision tree classifier |
| title_fullStr | Comparing the knowledge quality in rough classifier and decision tree classifier |
| title_full_unstemmed | Comparing the knowledge quality in rough classifier and decision tree classifier |
| title_short | Comparing the knowledge quality in rough classifier and decision tree classifier |
| title_sort | comparing the knowledge quality in rough classifier and decision tree classifier |
| topic | QA76 Computer software |
| url | https://repo.uum.edu.my/id/eprint/14121/1/04631700.pdf |
| work_keys_str_mv | AT mohamadmohsinmohamadfarhan comparingtheknowledgequalityinroughclassifieranddecisiontreeclassifier AT abdwahabmohdhelmy comparingtheknowledgequalityinroughclassifieranddecisiontreeclassifier |