Abstract
The classification of rare cases is a challenging problem in many real life applications. The scarcity of the rare cases makes it difficult for traditional classifiers to classify them correctly. In this paper, we propose a new approach to use emerging patterns (EPs) [3] in rare-class classification (EPRC). Traditional EP-based classifiers [2] fail to achieve accepted results when dealing with rare cases. EPRC overcomes this problem by applying three improving stages: generating new undiscovered EPs for the rare class, pruning low-support EPs, and increasing the supports of the rare-class EPs. An experimental evaluation carried out on a number of rare-class databases shows that EPRC outperforms EP-based classifiers as well as other classification methods such as PNrule [1], Metacost [6], and C4.5 [7].
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Alhammady, H., Ramamohanarao, K. (2004). The Application of Emerging Patterns for Improving the Quality of Rare-Class Classification. In: Dai, H., Srikant, R., Zhang, C. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2004. Lecture Notes in Computer Science(), vol 3056. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24775-3_27
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DOI: https://doi.org/10.1007/978-3-540-24775-3_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22064-0
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