On evaluating stream learning algorithms

By João Gama, Raquel Sebastião, Pedro Pereira Rodrigues. 2012

543 citations recorded by the source corpus.

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Citations in the corpus

Replay in Deep Learning: Current Approaches and Missing Biological Elements (2021, neutral): “Streaming learning is a special case of incremental batch learning where the batch size is set to one ( N t = 1) and the agent is allowed only a single epoch through the labeled training dataset (Gama, 2010; Gama, Sebastião, & Rodrigues, 2013).”
Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis (2019, background): “The ability to do online learning under these constraints from non-stationary data streams in a single pass is known as streaming learning [2, 4, 16, 19, 22, 23, 27, 43].”
Automatic Model Monitoring for Data Streams (2019, methodology): “One possibility that we are investigating is the use of forgetting factors, a technique that is often used in data streams [10].”
Online Machine Learning in Big Data Streams (2018, background): “Modeling is hence affected by potential concept drifts or changes in distribution [98].”
Online Semi-Supervised Learning with Deep Hybrid Boltzmann Machines and Denoising Autoencoders (2015, methodology): “995 (following the discussion of admissible values in Gama et al. (2012)).”
From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors (2022, background): “These approaches typically trace deviations in the online learner’s output error, known as the predictive sequential (prequential) error [27], to detect changes [28].”
Online and Non-Parametric Drift Detection Methods Based on Hoeffding’s Bounds (2015, methodology): “[27] propose a framework for evaluating”
Learning under Concept Drift: A Review (2019, background): “There are also studies [90], [91] that focus on adapting to concept drift in circumstances where true label data is completely unavailable.”
Distribution-Free One-Pass Learning (2017, background): “In contrast to previous one-pass approaches [6], [7], [8], our proposal considers to facilitate the learning system with capability of adapting the distribution change that often occurs during the data accumulation process, which is also one of fundamental requirements of learnware [17].”
Open challenges for data stream mining research (2014, methodology): “[16] proposed a way of calculating classification accuracy using only the most recent stream examples, therefore al-”
Evaluating time series forecasting models: an empirical study on performance estimation methods (2019, background): “[15] provide a thorough overview of the evaluation of predictive models for data streams mining.”