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A New Method for Data Stream Mining Based on the Misclassification Error
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DOI：10.1109/TNNLS.2014.2333557.png)
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Abstract
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In this paper, a new method for constructing decision trees for stream data is proposed. First a new splitting criterion based on the misclassification error is derived. A theorem is proven showing that the best attribute computed in considered node according to the available data sample is the same, with some high probability, as the attribute derived from the whole infinite data stream. Next this result is combined with the splitting criterion based on the Gini index. It is shown that such combination provides the highest accuracy among all studied algorithms.
Keywords:
Classification
data stream
decision trees
impurity measure
splitting criterion
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