Abstract
This paper proposes a framework for combining classifiers based on OWA operators in which each individual classifier uses a distinct representation of objects to be classified. It is shown that this framework yields several commonly used decision rules but without some strong assumptions made in the work by Kittler et al. [7]. As an application, we apply the proposed framework of classifier combination to the problem of word sense disambiguation (shortly, WSD). To this end, we experimentally design a set of individual classifiers, each of which corresponds to a distinct representation type of context considered in the WSD literature, and then the proposed combination strategies are experimentally tested on the datasets for four polysemous words, namely interest, line, serve, and hard, and compared to previous studies.
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Le, C.A., Huynh, VN., Dam, HC., Shimazu, A. (2005). Combining Classifiers Based on OWA Operators with an Application to Word Sense Disambiguation. In: Ślęzak, D., Wang, G., Szczuka, M., Düntsch, I., Yao, Y. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3641. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548669_53
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DOI: https://doi.org/10.1007/11548669_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28653-0
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