<p>This thesis presents the results of the implementation and evaluation of two machine learning algorithms [Baz98, GB97]based on notions from Rough Set theory [Paw82]. Both algorithms were implemented and tested using the Weka [WF00]software framework. The main purpose for doing this was to investigate whether the experimental results obtained in [Baz98]could be reproduced, by implementing both algorithms in a framework that provided common functionalities needed by both. As a result of this thesis, a Rough Set framework accompanying the Weka system was designed and implemented, as well as three methods for discretization and three classi cation methods. </p><p>The results of the evaluation did not match those obtained by the original authors. On two standard benchmarking datasets also used previously in [Baz98](Breast Cancer and Lymphography), signi cant results indicating that one of the algorithms performed better than the other could not be established, using the Students t- test and a con dence limit of 95%. However, on two other datasets (Balance Scale and Zoo) differences could be established with more than 95% signi cance. The Dynamic Reduct Approach scored better on the Balance Scale dataset whilst the LEM2 Approach scored better on the Zoo dataset.</p>
| Identifer | oai:union.ndltd.org:UPSALLA/oai:DiVA.org:liu-1856 |
| Date | January 2002 |
| Creators | Leifler, Ola |
| Publisher | Linköping University, Department of Computer and Information Science, Institutionen för datavetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | Swedish |
| Detected Language | English |
| Type | Student thesis, text |
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