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Feature selection is among the keys in many applications, especially in mining high-dimensional data. With lack of labeled instances, the learning accuracy may deteriorate using traditional methods. In this paper, we introduce a ldquowrapperrdquo type semi-supervised feature selection approach based on RSC model. It extends the class label from labeled training set to unlabeled data. Additionally,...
Burst detection is an inherent problem for data streams, so it has attracted extensive attention in research community due to its broad applications. One of the basic problems in burst detection is how to count frequencies of all elements in data stream. This paper presents a novel solution based on Improved Counting Bloom Filter, which is also called BCBF+HSet. Comparing with intuitionistic approach...
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