Reactive Soft Prototype Computing for Concept Drift Streams
نویسندگان
چکیده
منابع مشابه
Learning from Data Streams with Concept Drift Learning from Data Streams with Concept Drift
SUMMARY Increasing access to large, nonstationary datasets and corresponding demands to analyze these data has led to the development of new online algorithms for performing machine learning on data streams. An important feature of many real-world data streams is " concept drii, " whereby the characteristics of the data can change arbitrarily over time. e presence of concept drii in a data stre...
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Increasing access to incredibly large, nonstationary datasets and corresponding demands to analyse these data has led to the development of new online algorithms for performing machine learning on data streams. An important feature of real-world data streams is " concept drift, " whereby the distributions underlying the data can change arbitrarily over time. The presence of concept drift in a d...
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Learning on data streams subject to concept drifts is a challenging task. A successful algorithm must keep memory consumption constant regardless of the amount of data processed, and at the same time, retain good adaptation and prediction capabilities by effectively selecting which observations should be stored into memory. We claim that, instead of using a temporal window to discard observatio...
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In this paper we study the problem of constructing accurate block-based ensemble classifiers from time evolving data streams. AWE is the best-known representative of these ensembles. We propose a new algorithm called Accuracy Updated Ensemble (AUE), which extends AWE by using online component classifiers and updating them according to the current distribution. Additional modifications of weight...
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ژورنال
عنوان ژورنال: Neurocomputing
سال: 2020
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2019.11.111