Bachelor and Master Theses

Title: Combining different feature weighting methods for case-based reasoning
Subject: Computer Science
Level: Advanced
Description: The essential assumption in case-based reasoning (CBR) is that similar cases have similar solutions. The principle of a CBR system is to solve a new problem by reusing previous experiences. It is apparent that the accuracy of solutions produced by CBR systems depends on the criteria to assess similar cases and the method to produce a final result. So far quite a lot method has been proposed for different application domains. However, by using different methods, the accuracy of solutions may be different. In this thesis, we intend to investigate a much more reliable process to produce the solution for a new problem. The main idea of our approach is to adopt a fusion algorithm, which takes advantage of suggestions from different information sources in final decision making. It is therefore important for us to utilize different algorithms which are available to be implemented in the CBR field. Considering that the similarity between cases is affected by feature weights, two different algorithms for deriving feature weights are introduced in our thesis. Particularly, one of them is used to measure feature weights in terms of the nearest neighbor principle. The other is designed to solve classification problems by considering the flips of results. With the corresponding solutions from these algorithms, the fusion method is employed to produce a more reliable result. The specific way used in the fusion process is inspired from a disjunctive combination of degrees of possibilities of candidate solutions. Furthermore, we applied this approach to classification problems in several benchmark databases. The performance of this fusion approach has been presented by testing results in comparisons with other methods. Moreover, the influence of the case base size and the number of neighbors is also illustrated by implementing the three methods in different databases.
Prel. end date: 2014-11-25
Presentation date: 2014-06-13
Student: Bofeng Li
Student: Ling Lu
IDT supervisor: Ning Xiong, +46-21-151716
Examinator: Peter Funk
Peter Funk, +46-21-103153

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2014-11-25, 14:48

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