Abstract
Automatic induction from examples has a long tradition and represents an important technique used in data mining. Trough induction a method builds a hypothesis to explain observed facts. Many knowledge extraction methods have been developed, unfortunately each has advantages and limitations and in general there is no such method that would outperform all others on all problems. One of the possible approaches to overcome this problem is to combine different methods in one hybrid method. Recent research is mainly focused on a specific combination of methods, contrary, multimethod approach combines different induction methods in an unique manner – it applies different methods on the same knowledge model in no predefined order where each method may contain inherent limitations with the expectation that the combined multiple methods may produce better results. In this paper we present the overview of an idea, concrete integration and possible improvements.
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Lenič, M., Kokol, P., Zorman, M., Povalej, P., Stiglic, B., Yamamoto, R. Improved Knowledge Mining with the Multimethod Approach. In: Young Lin, T., Ohsuga, S., Liau, CJ., Hu, X., Tsumoto, S. (eds) Foundations of Data Mining and knowledge Discovery. Studies in Computational Intelligence, vol 6. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11498186_17
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DOI: https://doi.org/10.1007/11498186_17
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Publisher Name: Springer, Berlin, Heidelberg
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Online ISBN: 978-3-540-32408-9
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