Emerging Pattern Based Classification

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The first approach to consider emerging-pattern based classification is CAEP (classification by aggregating emerging patterns) [2]. The main idea is to aggregate (sum) the discriminating power of many of the emerging patterns contained in a case to be classified. The discriminating power of an emerging pattern is often reflected in the support difference of the pattern in the opposing classes. For each class, the emerging patterns of that class contained in the case are aggregated to form a score; the class with the highest score is deemed to be the class of the case. Score normalization can be used to deal with data/battern imbalance between classes. This classification method can lead to high quality classifiers, comparable or better than other classifiers.

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