Dynamic knowledge validation and verification for CBR teledermatology system
Objective: Case-based reasoning has been of great importance in the development of many decision support applications. However, relatively little effort has gone into investigating how new knowledge can be validated. Knowledge validation is important in dealing with imperfect data collected over tim...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Elsevier Science
2007
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| Online Access: | http://hdl.handle.net/20.500.11937/30727 |
| _version_ | 1848753171658178560 |
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| author | Ou, Monica H. Lazarescu, Mihai West, Geoffrey Clay, C. |
| author_facet | Ou, Monica H. Lazarescu, Mihai West, Geoffrey Clay, C. |
| author_sort | Ou, Monica H. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Objective: Case-based reasoning has been of great importance in the development of many decision support applications. However, relatively little effort has gone into investigating how new knowledge can be validated. Knowledge validation is important in dealing with imperfect data collected over time, because inconsistencies in data do occur and adversely affect the performance of a diagnostic system.Methods: This paper consists of two parts. First, it describes methods that enable the domain expert, who may not be familiar with machine learning, to interactively validate knowledge base of a Web-based teledermatology system. The validation techniques involve decision tree classification and formal concept analysis. Second, it describes techniques to discover unusual relationships hidden in the dataset for building and updating a comprehensive knowledge base, because the diagnostic performance of the system is highly dependent on the content thereof. Therefore, in order to classify different kinds of diseases, it is desirable to have a knowledge base that covers common as well as uncommon diagnoses.Results and conclusion: Evaluation results show that the knowledge validation techniques are effective in keeping the knowledge base consistent, and that the query refinement techniques are useful in improving the comprehensiveness of the case base. |
| first_indexed | 2025-11-14T08:20:17Z |
| format | Journal Article |
| id | curtin-20.500.11937-30727 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:20:17Z |
| publishDate | 2007 |
| publisher | Elsevier Science |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-307272017-09-13T15:55:52Z Dynamic knowledge validation and verification for CBR teledermatology system Ou, Monica H. Lazarescu, Mihai West, Geoffrey Clay, C. Objective: Case-based reasoning has been of great importance in the development of many decision support applications. However, relatively little effort has gone into investigating how new knowledge can be validated. Knowledge validation is important in dealing with imperfect data collected over time, because inconsistencies in data do occur and adversely affect the performance of a diagnostic system.Methods: This paper consists of two parts. First, it describes methods that enable the domain expert, who may not be familiar with machine learning, to interactively validate knowledge base of a Web-based teledermatology system. The validation techniques involve decision tree classification and formal concept analysis. Second, it describes techniques to discover unusual relationships hidden in the dataset for building and updating a comprehensive knowledge base, because the diagnostic performance of the system is highly dependent on the content thereof. Therefore, in order to classify different kinds of diseases, it is desirable to have a knowledge base that covers common as well as uncommon diagnoses.Results and conclusion: Evaluation results show that the knowledge validation techniques are effective in keeping the knowledge base consistent, and that the query refinement techniques are useful in improving the comprehensiveness of the case base. 2007 Journal Article http://hdl.handle.net/20.500.11937/30727 10.1016/j.artmed.2006.08.004 Elsevier Science restricted |
| spellingShingle | Ou, Monica H. Lazarescu, Mihai West, Geoffrey Clay, C. Dynamic knowledge validation and verification for CBR teledermatology system |
| title | Dynamic knowledge validation and verification for CBR teledermatology system |
| title_full | Dynamic knowledge validation and verification for CBR teledermatology system |
| title_fullStr | Dynamic knowledge validation and verification for CBR teledermatology system |
| title_full_unstemmed | Dynamic knowledge validation and verification for CBR teledermatology system |
| title_short | Dynamic knowledge validation and verification for CBR teledermatology system |
| title_sort | dynamic knowledge validation and verification for cbr teledermatology system |
| url | http://hdl.handle.net/20.500.11937/30727 |