Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Multi-disciplinary Studies | en_US |
| dc.contributor | Department of Computing | en_US |
| dc.creator | Ma, Yuk-kwong Ben | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/3569 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | Case base maintenance by using artificial neural network | en_US |
| dcterms.abstract | The real world applications of the Case-Based Reasoning systems required not only a satisfactory competence but sufficient solving efficiency too. The solving efficiency concerns the memory and response time limitations. The larger the case library, the more the problem space covered, however, it would also downgrade the system performance if the number of cases grows to an unacceptable high level. In order to limit the case base size, it is necessary what kind of case bases can be stored. This report proposes an approach of selecting representatives cases. This will involve two phases. The first phase aims to select useful feature out from all fields involved. It will involve a traditional Neural Network. After the training, then it will generate a fuzzy set defined on the cluster space for each record. The second phase aims to use different approach as a guideline for deletion of cases. It will involve the calculation of fuzzy class membership degree of each record as well as their similarity and case density. A mixed of them will also be calculated and serve as a guideline for apply the deletion. Several testing database is used as an illustration of our approach. And overall result shows that the combination of feature weight selection at the first phase and mixed of degree membership and case density at the second phase will generate the best result in which it could reduce the size of the case library by around 25% and the overall knowledge coverage will remain 90%. Future work will include integrating adaptation rules for building deletion policy. | en_US |
| dcterms.extent | 80 leaves : ill. ; 30 cm | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2000 | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.LCSH | Case-based reasoning | en_US |
| dcterms.LCSH | Neural networks (Computer science) | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b15321988.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 4.03 MB | Adobe PDF | View/Open |
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