Abstract
Energy consumption reduction is an increasing trend in machine learning given its relevance in socio-ecological importance. Consequently, it is important to quantify how real-time learning algorithms tailored for data streams and edge computing behave in terms of accuracy, processing time, memory usage, and energy consumption. In this work, we bring forward a tool for measuring energy consumption in the Massive Online Analysis (MOA). First, we analyze the energy consumption rates obtained by our tool against a gold-standard hardware solution, thus showing the robustness of our approach. Next, we experimentally analyze classification algorithms under different validation protocols and concept drift and highlight how such classifiers behave under such conditions. Results show that our tools enable the identification of different classifiers’ energy consumption. In particular, it allows a better understanding of how energy consumption rates vary in drifting and non-drifting scenarios. Finally, given the insights obtained during experimentation on existing classifiers, we make our tool publicly available to the scientific community so that energy consumption is also accounted for in developing and comparing data stream mining algorithms.
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Onuki, E.K.T., Malucelli, A., Barddal, J.P. (2023). A Tool for Measuring Energy Consumption in Data Stream Mining. In: Naldi, M.C., Bianchi, R.A.C. (eds) Intelligent Systems. BRACIS 2023. Lecture Notes in Computer Science(), vol 14197. Springer, Cham. https://doi.org/10.1007/978-3-031-45392-2_28
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