A New Method for Data Stream Mining Based on the Misclassification Error

作者：Rutkowski Leszek^*; Jaworski Maciej; Pietruczuk Lena; Duda Piotr

来源：IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(5): 1048-1059.

DOI：10.1109/TNNLS.2014.2333557

• Classification
• data stream
• decision trees
• impurity measure
• splitting criterion

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.

• 出版日期2015-5
• 单位中国社会科学院