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Evolving associative classifier for incomplete database using genetic network programming

Published: 12 July 2011 Publication History

Abstract

An evolving classification method for incomplete database has been proposed as an extension of Genetic Network programming (GNP) based rule extraction. An incomplete database includes missing values, however, the method can extract class association rules and build a classifier. The proposed method evolves the classifier using the labeled instances by itself as acquired information. We have evaluated the performance of the proposed method using artificial incomplete data set. The results showed that the proposed method has a potential of gathering useful information for classification through its evolutionary process.

References

[1]
K. Shimada and K. Hirasawa, "A Method of Association Rule Analysis for Incomplete Database Using Genetic Network Programming", in Proc. of the Genetic and Evolutionary Computation Conference 2010 (GECCO2010), pp. 1115--1122, 2010.
[2]
K. Shimada, K. Hirasawa and J. Hu, "Class Association Rule Mining with Chi-Squared Test Using Genetic Network Programming", in Proc. of the IEEE Conf. on Systems, Man, and Cybernetics, pp. 5338--5344, 2006.

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  1. Evolving associative classifier for incomplete database using genetic network programming

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    cover image ACM Conferences
    GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
    July 2011
    1548 pages
    ISBN:9781450306904
    DOI:10.1145/2001858

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    New York, NY, United States

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    Published: 12 July 2011

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    Author Tags

    1. association rule
    2. classification
    3. genetic network programming

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