Abstract
Mining emerging patterns (EPs) in rare-class databases is one of the new and difficult problems in knowledge discovery in databases (KDD). The main challenge in this task is the limited number of rare-class instances. This scarcity limits the number of emerging patterns that can be mined for the rare class. In this paper, we propose a novel approach for mining emerging patterns in rare-class datasets. We experimentally prove that our method is capable of gaining enough knowledge from the rare class; hence, it increases the performance of EP-based classifiers.
Keywords
- Class Label
- Major Class
- Edible Mushroom
- High Predictive Power
- Emerge Pattern
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Alhammady, H. (2007). A Novel Approach For Mining Emerging Patterns In Rare-Class Datasets. In: Sobh, T. (eds) Innovations and Advanced Techniques in Computer and Information Sciences and Engineering. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6268-1_38
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DOI: https://doi.org/10.1007/978-1-4020-6268-1_38
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-6267-4
Online ISBN: 978-1-4020-6268-1
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