Enabling Fast Lazy Learning for Data Streams

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How researchers see this paper

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Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, methodology): “Lazy learning-based approach has widely been used in several practical contexts, such as data streams [87], air quality planning [88], predictive toxicology based on a chemical ontology [89], prediction of customer demands [90], hydrologic forecasting [91], air quality prediction [92], magne-”
A Survey on Ensemble Learning for Data Stream Classification (2017, methodology): “2012]; Instance-based (Lazy) methods [Beringer and Hüllermeier 2007; Zhang et al. 2011a]; and ensemble classifiers [Oza 2005; Bifet et al.”
A survey on data stream clustering and classification (2015, background): “[104] proposed a Lazy-tree structure to index microclusters (which are called exemplars in the paper).”