Human-like rule optimization for continuous domains
When using machine learning techniques for data mining purposes one of the main requirements is that the learned rule set is represented in a comprehensible form. Simpler rules are preferred as they are expected to perform better on unseen data. At the same time the rules should be specific enough s...
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Springer
2008
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| Online Access: | http://hdl.handle.net/20.500.11937/42310 |
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curtin-20.500.11937-42310 |
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eprints |
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curtin-20.500.11937-423102017-09-13T15:59:56Z Human-like rule optimization for continuous domains Hadzic, Fedja Dillon, Tharam S. Ana Fred, Joaquim Filipe and Hugo Gamboa Feature Selection Data Mining Rule Optimization When using machine learning techniques for data mining purposes one of the main requirements is that the learned rule set is represented in a comprehensible form. Simpler rules are preferred as they are expected to perform better on unseen data. At the same time the rules should be specific enough so that the misclassification rate is kept to a minimum. 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. The attributes detected as irrelevant can be removed and the ones previously detected as irrelevant can be reintroduced if necessary. The method is evaluated on the rules extracted from publicly available real world datasets using different classifiers, and the results demonstrate the effectiveness of the presented rule optimizing technique. 2008 Book Chapter http://hdl.handle.net/20.500.11937/42310 10.1007/978-3-540-92219-3_25 Springer fulltext |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
Curtin University Malaysia |
| building |
Curtin Institutional Repository |
| collection |
Online Access |
| topic |
Feature Selection Data Mining Rule Optimization |
| spellingShingle |
Feature Selection Data Mining Rule Optimization Hadzic, Fedja Dillon, Tharam S. Human-like rule optimization for continuous domains |
| description |
When using machine learning techniques for data mining purposes one of the main requirements is that the learned rule set is represented in a comprehensible form. Simpler rules are preferred as they are expected to perform better on unseen data. At the same time the rules should be specific enough so that the misclassification rate is kept to a minimum. 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. The attributes detected as irrelevant can be removed and the ones previously detected as irrelevant can be reintroduced if necessary. The method is evaluated on the rules extracted from publicly available real world datasets using different classifiers, and the results demonstrate the effectiveness of the presented rule optimizing technique. |
| author2 |
Ana Fred, Joaquim Filipe and Hugo Gamboa |
| author_facet |
Ana Fred, Joaquim Filipe and Hugo Gamboa Hadzic, Fedja Dillon, Tharam S. |
| format |
Book Chapter |
| author |
Hadzic, Fedja Dillon, Tharam S. |
| author_sort |
Hadzic, Fedja |
| title |
Human-like rule optimization for continuous domains |
| title_short |
Human-like rule optimization for continuous domains |
| title_full |
Human-like rule optimization for continuous domains |
| title_fullStr |
Human-like rule optimization for continuous domains |
| title_full_unstemmed |
Human-like rule optimization for continuous domains |
| title_sort |
human-like rule optimization for continuous domains |
| publisher |
Springer |
| publishDate |
2008 |
| url |
http://hdl.handle.net/20.500.11937/42310 |
| first_indexed |
2018-09-06T23:19:24Z |
| last_indexed |
2018-09-06T23:19:24Z |
| _version_ |
1610902105603702784 |