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Rutkowski L, Jaworski M, Pietruczuk L, et al. A new method for data stream mining based on the misclassification error. IEEE Trans Neural Netw Learn Syst. 2014;26(5):1048-59doi: 10.1109/TNNLS.2014.2333557.
Rutkowski, L., Jaworski, M., Pietruczuk, L., & Duda, P. (2015). A new method for data stream mining based on the misclassification error. IEEE transactions on neural networks and learning systems, 26(5), 1048-59. https://doi.org/10.1109/TNNLS.2014.2333557
Rutkowski, Leszek, et al. "A new method for data stream mining based on the misclassification error." IEEE transactions on neural networks and learning systems vol. 26,5 (2015): 1048-59. doi: https://doi.org/10.1109/TNNLS.2014.2333557
Rutkowski L, Jaworski M, Pietruczuk L, Duda P. A new method for data stream mining based on the misclassification error. IEEE Trans Neural Netw Learn Syst. 2015 May;26(5):1048-59. doi: 10.1109/TNNLS.2014.2333557. Epub 2014 Jul 16. PMID: 25051560.
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IEEE Trans Neural Netw Learn Syst. 2015 May;26(5):1048-59. doi: 10.1109/TNNLS.2014.2333557. Epub 2014 Jul 16.

A new method for data stream mining based on the misclassification error.

IEEE transactions on neural networks and learning systems

Leszek Rutkowski, Maciej Jaworski, Lena Pietruczuk, Piotr Duda

PMID: 25051560 DOI: 10.1109/TNNLS.2014.2333557

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.

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