Title :
A New Method for Data Stream Mining Based on the Misclassification Error
Author :
Rutkowski, Leszek ; Jaworski, Maciej ; Pietruczuk, Lena ; Duda, Piotr
Author_Institution :
Inst. of Comput. Intell., Czestochowa Univ. of Technol., Czestochowa, Poland
Abstract :
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 :
data mining; decision trees; pattern classification; Gini index; data stream mining; decision tree; misclassification error; splitting criterion; Accuracy; Data mining; Decision trees; Gaussian distribution; Impurities; Indexes; Silicon; Classification; data stream; decision trees; impurity measure; splitting criterion; splitting criterion.;
Journal_Title :
Neural Networks and Learning Systems, IEEE Transactions on
DOI :
10.1109/TNNLS.2014.2333557
