Hierarchical sequential testing8/4/2023 ![]() In increasing number of real world applications, data are presented as streams that may evolve overtime and this is known by concept drift. Recent advances in Computational Intelligent Systems have focused on addressing complex problems related to the dynamicity of the environments. Finally, we also provide an inventory of existing real and synthetic datasets, as well as tools and software for getting started, evaluating and comparing different approaches. In this effort, we provide a comprehensive survey and tutorial of established as well as state-of-the-art approaches, while highlighting two primary perspectives, active and passive, for learning in nonstationary environments. Learning in nonstationary environments requires adaptive or evolving approaches that can monitor and track the underlying changes, and adapt a model to accommodate those changes accordingly. Therefore, the fundamental and rather naïve assumption made by most computational intelligence approaches – that the training and testing data are sampled from the same fixed, albeit unknown, probability distribution – is simply not true. In nonstationary environments, particularly those that generate streaming or multi-domain data, the probability density function of the data-generating process may change (drift) over time. Examples of these applications include making inferences or predictions based on financial data, energy demand and climate data analysis, web usage or sensor network monitoring, and malware/spam detection, among many others. Applications that generate data from nonstationary environments, where the underlying phenomena change over time, are becoming increasingly prevalent.
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