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A Hybrid heuristic-exhaustive search approach for rule extraction
Dissertation (MSc)--University of Pretoria, 2007.
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| Format: | Thesis |
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University of Pretoria
2013
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| _version_ | 1867613448449294336 |
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| access_status_str | Open Access |
| author2 | Engelbrecht, Andries P. |
| author_browse | Engelbrecht, Andries P. |
| author_facet | Engelbrecht, Andries P. |
| collection | Thesis |
| dc_rights_str_mv | © 2000, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MSc)--University of Pretoria, 2007. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/25095 |
| institution | University of Pretoria (South Africa) |
| last_indexed | 2026-06-10T12:36:18.633Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2013 |
| publishDateRange | 2013 |
| publishDateSort | 2013 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/25095 A Hybrid heuristic-exhaustive search approach for rule extraction Engelbrecht, Andries P. Rodic, Daniel Hybrid classifier system Artificial intelligence Data mining algorithms Hcs Automated knowledge UCTD Dissertation (MSc)--University of Pretoria, 2007. The topic of this thesis is knowledge discovery and artificial intelligence based knowledge discovery algorithms. The knowledge discovery process and associated problems are discussed, followed by an overview of three classes of artificial intelligence based knowledge discovery algorithms. Typical representatives of each of these classes are presented and discussed in greater detail. Then a new knowledge discovery algorithm, called Hybrid Classifier System (HCS), is presented. The guiding concept behind the new algorithm was simplicity. The new knowledge discovery algorithm is loosely based on schemata theory. It is evaluated against one of the discussed algorithms from each class, namely: CN2; C4.5, BRAINNE and BGP. Results are discussed and compared. A comparison was done using a benchmark of classification problems. These results show that the new knowledge discovery algorithm performs satisfactory, yielding accurate, crisp rule sets. Probably the main strength of the HCS algorithm is its simplicity, so it can be the foundation for many possible future extensions. Some of the possible extensions of the new proposed algorithm are suggested in the final part of this thesis. Computer Science unrestricted 2013-09-06T19:11:51Z 2006-06-05 2013-09-06T19:11:51Z 2001-04-01 2007-06-05 2006-05-29 Dissertation Rodic, D 2000, A hybrid heuristic-exhaustive search approach for rule extraction, MEng dissertation, University of Pretoria, Pretoria, viewed yymmdd < http://hdl.handle.net/2263/25095 > H558/ag http://hdl.handle.net/2263/25095 http://upetd.up.ac.za/thesis/available/etd-05292006-110006/ © 2000, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Hybrid classifier system Artificial intelligence Data mining algorithms Hcs Automated knowledge UCTD A Hybrid heuristic-exhaustive search approach for rule extraction |
| title | A Hybrid heuristic-exhaustive search approach for rule extraction |
| title_full | A Hybrid heuristic-exhaustive search approach for rule extraction |
| title_fullStr | A Hybrid heuristic-exhaustive search approach for rule extraction |
| title_full_unstemmed | A Hybrid heuristic-exhaustive search approach for rule extraction |
| title_short | A Hybrid heuristic-exhaustive search approach for rule extraction |
| title_sort | hybrid heuristic exhaustive search approach for rule extraction |
| topic | Hybrid classifier system Artificial intelligence Data mining algorithms Hcs Automated knowledge UCTD |
| url | http://hdl.handle.net/2263/25095 http://upetd.up.ac.za/thesis/available/etd-05292006-110006/ |