Abstract
Classification has been one of the focal points in data mining research and applications. With an effective approach to building compact and accurate associative classification (namely GARC – Gain-based Association Rule Classification (Chen, Liu, Yu, Wei, & Zhang, 2006)) in forms of association rules, this chapter explores a way of fuzzy extension to GARC in dealing with the problem caused by crisp partitions for continuous attribute domains in data. Concretely, the sharp boundaries of the partitioned intervals are smoothened using fuzzy sets (or often conveniently labeled in linguistic terms) so as to reflect a variety of fuzziness on the domains (parameterized in f2), giving rise to a fuzzy associative classifier (i.e., GARC f2). Furthermore, due to the fuzziness involved, the notions of information gain, rule redundancy and conflicts are extended, aimed at providing the desirable features of GARC in the fuzzy extension context for accuracy and compactness. Moreover, data experiments on benchmarking datasets as well as a real-world application illustrate the effectiveness of the proposed fuzzy associative classifier.
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Chen, G., Xiong, Y., Wei, Q. (2010). A Fuzzy Extension to Compact and Accurate Associative Classification. In: Cornelis, C., Deschrijver, G., Nachtegael, M., Schockaert, S., Shi, Y. (eds) 35 Years of Fuzzy Set Theory. Studies in Fuzziness and Soft Computing, vol 261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16629-7_9
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DOI: https://doi.org/10.1007/978-3-642-16629-7_9
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