Case base maintenance by using artificial neural network

Pao Yue-kong Library Electronic Theses Database

Case base maintenance by using artificial neural network

 

Author: Ma, Yuk-kwong Ben
Title: Case base maintenance by using artificial neural network
Degree: M.Sc.
Year: 2000
Subject: Case-based reasoning
Neural networks (Computer science)
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Dept. of Computing
Pages: 80 leaves : ill. ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1532198
URI: http://theses.lib.polyu.edu.hk/handle/200/3569
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.

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