Data Stream Mining Based on the Misclassification Error

Information Science and Engineering

NATIONAL CONFERENCE on VLSI, COMMUNICATION AND SIGNAL PROCESSING (NCVCS-2017)

In recent years, the amount of data that needs to be analyzed is growing very fast. Potentially unlimited number of data is the cause of creation of a new field of research called data stream mining. By analyzing stream of data elements, one has to face new difficulties, therefore standard approach to the problem of data mining cannot be applied. 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.

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