Abstract
An associative classification method for incomplete database is proposed based on an evolutionary rule extraction method. The method can extract class association rules directly from the database including missing values and build an associative classifier. Instances including missing values are classified by the classifier. In addition, an evolving associative classifier is proposed. The proposed method evolves the classifier using the labeled instances by itself as acquired information. The performance of the classification was evaluated using artificial incomplete data set. The results showed that the proposed evolving associative classifier has a potential to expand the target data for classification through its evolutionary process and gather useful information itself.
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Shimada, K. (2012). An Evolving Associative Classifier for Incomplete Database. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2012. Lecture Notes in Computer Science(), vol 7377. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31488-9_12
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DOI: https://doi.org/10.1007/978-3-642-31488-9_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31487-2
Online ISBN: 978-3-642-31488-9
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