Abstract
Associative classifiers have been the subject of intense research for the last few years. Experiments have shown that they generally result in higher accuracy than decision tree classifiers. In this paper, we introduce a novel algorithm for associative classification “Classification based on Association Rules Generated in a Bidirectional Apporach” (CARGBA). It generates rules in two steps. At first, it generates a set of high confidence rules of smaller length with support pruning and then augments this set with some high confidence rules of higher length with support below minimum support. Experiments on 6 datasets show that our approach achieves better accuracy than other state-of-the-art associative classification algorithms.
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Kundu, G., Munir, S., Bari, M.F., Islam, M.M., Murase, K. (2008). A Novel Algorithm for Associative Classification. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4985. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69162-4_47
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DOI: https://doi.org/10.1007/978-3-540-69162-4_47
Publisher Name: Springer, Berlin, Heidelberg
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