Abstract
Classification is an important task in data mining. Contrast patterns, such as emerging patterns, have been shown to be powerful for building classifiers, but they rarely exist in sparse data. Recently proposed disjunctive emerging patterns are highly expressive, and can potentially overcome this limitation. Simple contrast patterns only allow simple conjunctions, whereas disjunctive patterns additionally allow expressions of disjunctions. This paper investigates whether expressive contrasts are beneficial for classification. We adopt a statistical methodology for eliminating noisy patterns. Our experiments identify circumstances where expressive patterns can improve over previous contrast pattern based classifiers. We also present some guidelines for i) using expressive patterns based on the nature of the given data, ii) how to choose between the different types of contrast patterns for building a classifier.
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Loekito, E., Bailey, J. (2009). Using Highly Expressive Contrast Patterns for Classification - Is It Worthwhile?. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, TB. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2009. Lecture Notes in Computer Science(), vol 5476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01307-2_44
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DOI: https://doi.org/10.1007/978-3-642-01307-2_44
Publisher Name: Springer, Berlin, Heidelberg
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