Abstract
Data Stream represents a significant challenge for data analysis and data mining techniques because those techniques are developed based on training batch data. Classification technique that deals with data stream should have the ability for adapting its model for the new samples and forget the old ones. In this paper, we present an intensive comparison for the performance of six of popular classification techniques and focusing on the power of Adaptive Random Forest. The comparison was made based on four real medical datasets and for more reliable results, 40 other datasets were made by adding white noise to the original datasets. The experimental results showed the dominant of Adaptive Random Forest over five other techniques with high robustness against the change in data and noise.
A. Kiss was also with J. Selye University, Komarno, Slovakia.
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Acknowledgment
The project was supported by the European Union, co-financed by the European Social Fund (EFOP-3.6.3-VEKOP-16-2017-00002).
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Fatlawi, H.K., Kiss, A. (2020). On Robustness of Adaptive Random Forest Classifier on Biomedical Data Stream. In: Nguyen, N., Jearanaitanakij, K., Selamat, A., Trawiński, B., Chittayasothorn, S. (eds) Intelligent Information and Database Systems. ACIIDS 2020. Lecture Notes in Computer Science(), vol 12033. Springer, Cham. https://doi.org/10.1007/978-3-030-41964-6_29
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