Abstract
The main objective of this work is to evaluate the usefulness of the machine learning approach to knowledge acquisition. A series of experiments was done to check the quality of rule sets induced by the LERS system, which has four options; two of them represent the machine learning approach and the remaining two represent the knowledge acquisition approach. The six real-life data sets were modified to simulate incomplete knowledge. As a result it is clear that machine learning options performed much worse than knowledge acquisition options.
The final conclusion is that machine learning methods used so far for rule induction in knowledge acquisition should be replaced by other methods of rule induction that will generate complete sets of rules. Knowledge acquisition options of LERS are examples of such appropriate ways of inducing rules for building knowledge bases.
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© 1994 British Computer Society
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Grzymala-Busse, D.M., Grzymala-Busse, J.W. (1994). Comparison of Machine Learning and Knowledge Acquisition Methods of Rule Induction Based on Rough Sets. In: Ziarko, W.P. (eds) Rough Sets, Fuzzy Sets and Knowledge Discovery. Workshops in Computing. Springer, London. https://doi.org/10.1007/978-1-4471-3238-7_34
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DOI: https://doi.org/10.1007/978-1-4471-3238-7_34
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