Instance-Based Classification by Emerging Patterns
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Emerging patterns (EPs), namely itemsets whose supports change significantly from one class to another, capture discriminating features that sharply contrast instances between the classes. Recently, EP-based classifiers have been proposed, which first mine as many EPs as possible (called eager-learning) from the training data and then aggregate the discriminating power of the mined EPs for classifying new instances. We propose here a new, instance-based classifier using EPs, called DeEPs, to achieve much better accuracy and efficiency than the previously proposed EP-based classifiers. High accuracy is achieved because the instance-based approach enables DeEPs to pinpoint all EPs relevant to a test instance, some of which are missed by the eager-learning approaches. High efficiency is obtained using a series of data reduction and concise data-representation techniques. Experiments show that DeEPs’ decision time is linearly scalable over the number of training instances and nearly linearly over the number of attributes. Experiments on 40 datasets also show that DeEPs is superior to other classifiers on accuracy.
& Ramamohanarao, K.
(2000). Instance-Based Classification by Emerging Patterns. Lecture Notes in Computer Science, 1910, 191-200.