Abstract
Today, there is a tremendous growth in the amount of data being generated from various fields (such as smartphones, social networks, emails, customer click streams, different types of sensors and Internet of Things) that show Big Data attributes. Recently efforts have been made towards developing models for knowledge discovery from such data under the research area of stream mining or data stream classification in particular. Ensemble learners have become the popular approach in data stream classification because of their stability-elasticity property, which enables handling data stream challenges such as concept drift, recurrent concepts, novel class detection, and class imbalance. In this paper, we compare ten ensemble classifiers with respect to concept drift and class imbalance using Prequential AUC. In addition, Friedman nonparametric statistical test and Nemenyi post-hoc test were used to identify the best approach among them. This work to some extent can serve as part of a review of existing ensemble classifier algorithms for non-stationary data streams.
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Tambuwal, A.I., Neagu, D., Gheorghe, M. (2017). An Experimental Comparison of Ensemble Classifiers for Evolving Data Streams. In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXIV. SGAI 2017. Lecture Notes in Computer Science(), vol 10630. Springer, Cham. https://doi.org/10.1007/978-3-319-71078-5_14
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DOI: https://doi.org/10.1007/978-3-319-71078-5_14
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