Rule optimizing technique motivated by human concept formation
In this paper we present a rule optimizing technique motivated by the psychological studies of human concept learning. The technique allows for reasoning to happen at both higher levels of abstraction and lower level of detail in order to optimize the rule set. Information stored at the higher level...
| Main Authors: | , |
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| Format: | Conference Paper |
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Instinct Press
2008
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| Online Access: | http://www.biosignals.org http://hdl.handle.net/20.500.11937/41792 |
| _version_ | 1848756242069061632 |
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| author | Hadzic, Fedja Dillon, Tharam S. |
| author_facet | Hadzic, Fedja Dillon, Tharam S. |
| author_sort | Hadzic, Fedja |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper we present a rule optimizing technique motivated by the psychological studies of human concept learning. The technique allows for reasoning to happen at both higher levels of abstraction and lower level of detail in order to optimize the rule set. Information stored at the higher level allows for optimizing processes such as rule splitting, merging and deleting, while the information stored at the lower level allows for determining the attribute relevance for a particular rule |
| first_indexed | 2025-11-14T09:09:05Z |
| format | Conference Paper |
| id | curtin-20.500.11937-41792 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:09:05Z |
| publishDate | 2008 |
| publisher | Instinct Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-417922017-01-30T14:55:23Z Rule optimizing technique motivated by human concept formation Hadzic, Fedja Dillon, Tharam S. Feature Selection Data Mining Rule Optimization In this paper we present a rule optimizing technique motivated by the psychological studies of human concept learning. The technique allows for reasoning to happen at both higher levels of abstraction and lower level of detail in order to optimize the rule set. Information stored at the higher level allows for optimizing processes such as rule splitting, merging and deleting, while the information stored at the lower level allows for determining the attribute relevance for a particular rule 2008 Conference Paper http://hdl.handle.net/20.500.11937/41792 http://www.biosignals.org Instinct Press fulltext |
| spellingShingle | Feature Selection Data Mining Rule Optimization Hadzic, Fedja Dillon, Tharam S. Rule optimizing technique motivated by human concept formation |
| title | Rule optimizing technique motivated by human concept formation |
| title_full | Rule optimizing technique motivated by human concept formation |
| title_fullStr | Rule optimizing technique motivated by human concept formation |
| title_full_unstemmed | Rule optimizing technique motivated by human concept formation |
| title_short | Rule optimizing technique motivated by human concept formation |
| title_sort | rule optimizing technique motivated by human concept formation |
| topic | Feature Selection Data Mining Rule Optimization |
| url | http://www.biosignals.org http://hdl.handle.net/20.500.11937/41792 |