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Mining continuous classes using evolutionary computing
Dissertation (MSc)--University of Pretoria, 2006.
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| Format: | Thesis |
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University of Pretoria
2013
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| _version_ | 1867613524853784576 |
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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 | © 2002, 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, 2006. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/26528 |
| institution | University of Pretoria (South Africa) |
| last_indexed | 2026-06-10T12:37:31.320Z |
| 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:2263/26528 Mining continuous classes using evolutionary computing Engelbrecht, Andries P. Potgieter, Gavin Data mining UCTD Dissertation (MSc)--University of Pretoria, 2006. Data mining is the term given to knowledge discovery paradigms that attempt to infer knowledge, in the form of rules, from structured data using machine learning algorithms. Specifically, data mining attempts to infer rules that are accurate, crisp, comprehensible and interesting. There are not many data mining algorithms for mining continuous classes. This thesis develops a new approach for mining continuous classes. The approach is based on a genetic program, which utilises an efficient genetic algorithm approach to evolve the non-linear regressions described by the leaf nodes of individuals in the genetic program's population. The approach also optimises the learning process by using an efficient, fast data clustering algo¬rithm to reduce the training pattern search space. Experimental results from both algorithms are compared with results obtained from a neural network. The experimental results of the genetic program is also compared against a commercial data mining package (Cubist). These results indicate that the genetic algorithm technique is substantially faster than the neural network, and produces comparable accuracy. The genetic program produces substantially less complex rules than that of both the neural network and Cubist. Computer Science unrestricted 2013-09-07T06:26:34Z 2005-07-26 2013-09-07T06:26:34Z 2003-04-01 2006-07-26 2005-07-22 Dissertation Potgieter, G 2002, Mining continuous classes using evolutionary computing, MEng dissertation, University of Pretoria, Pretoria, viewed yymmdd < http://hdl.handle.net/2263/26528 > H678/ag http://hdl.handle.net/2263/26528 http://upetd.up.ac.za/thesis/available/etd-07222005-104751/ © 2002, 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 | Data mining UCTD Mining continuous classes using evolutionary computing |
| title | Mining continuous classes using evolutionary computing |
| title_full | Mining continuous classes using evolutionary computing |
| title_fullStr | Mining continuous classes using evolutionary computing |
| title_full_unstemmed | Mining continuous classes using evolutionary computing |
| title_short | Mining continuous classes using evolutionary computing |
| title_sort | mining continuous classes using evolutionary computing |
| topic | Data mining UCTD |
| url | http://hdl.handle.net/2263/26528 http://upetd.up.ac.za/thesis/available/etd-07222005-104751/ |