Abstract
Emerging Patterns (EPs) are those itemsets whose supports in one class are significantly higher than their supports in the other class. In this paper we investigate how to “bag” EP-based classifiers to build effective ensembles. We design a new scoring function based on growth rates to increase the diversity of individual classifiers and an effective scheme to combine the power of ensemble members. The experimental results confirm that our method of “bagging” EP-based classifiers can produce a more accurate and noise tolerant classifier ensemble.
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Fan, H., Fan, M., Ramamohanarao, K., Liu, M. (2006). Further Improving Emerging Pattern Based Classifiers Via Bagging. In: Ng, WK., Kitsuregawa, M., Li, J., Chang, K. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2006. Lecture Notes in Computer Science(), vol 3918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731139_13
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DOI: https://doi.org/10.1007/11731139_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33206-0
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