A Novel Concept Drift Detection Method for Incremental Learning in Nonstationary Environments

Yang, Zhe; Al‐Dahidi, Sameer; Baraldi, Piero; Zio, Enrico; Montelatici, L.

doi:10.1109/tnnls.2019.2900956

Cited by 78 publications

(39 citation statements)

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“…We allow for between-study heterogeneity, allowing both the underlying marginal distributions, f (| 𝕏 k ), f ( y k ) and the conditional distribution, f ( y k | | 𝕏 k ) to vary across studies. These are known in the dataset shift literature as “covariate shift” and “concept drift” respectively (Yang et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

“…This is in contrast to much of the concept/hybrid drift literature that deal with streams of data (often time series) that change over time (in one of the manners described above) and are typically from one source, such as environmental or energy data (Yang et al, 2019;Raza et al, 2019;Karnick et al, 2008;Ditzler et al, 2010), (i.e., f (X t ) and/or f (Y t |X t ) change over time). As such, many concept drift methods employ approaches that are inherently tailored to the temporal nature of the data streams, such as moving averages ( [Raza, et al, 2008]), detection systems to identify the presence ( [Yang, et al, 2019]) or speed ( [Minku, et al, 2009]) of a shift at a given instance, and ensemble methods that add, remove, or reweight classifiers across time ( [Raza, et al, 2008]; [Minku, et al, 2009]). While virtual drift work often does not assume changes over time, its methods usually center on reweighting observations ( [Sugiyama, et al, 2008]; ( [Shimodaira, 2000]) and assume that f (Y|X) remains constant between training and test sets.…”

Section: 2mentioning

confidence: 99%

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Hierachical Resampling for Bagging in Multi-Study Prediction with Applications to Human Neurochemical Sensing

Loewinger

Patil

Kishida

et al. 2019

Preprint

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Prediction settings with multiple studies have become increasingly common. Ensembling models trained on individual studies has been shown to improve replicability in new studies. Motivated by a groundbreaking new technology in human neuroscience, we introduce two generalizations of multi-study ensemble predictions. First, while existing methods weight ensemble elements by cross-study prediction performance, we extend weighting schemes to also incorporate covariate similarity between training data and target validation studies. Second, we introduce a hierarchical resampling scheme to generate pseudo-study replicates ("study straps") and ensemble classifiers trained on these rather than the original studies themselves. We demonstrate analytically that existing methods are special cases. Through a tuning parameter, our approach forms a continuum between merging all training data and training with existing multistudy ensembles. Leveraging this continuum helps accommodate different levels of between-study heterogeneity.Our methods are motivated by the application of Voltammetry in humans. This technique records electrical brain measurements and converts signals into neurotransmitter concentration estimates using a prediction model. Using this model in practice presents a crossstudy challenge, for which we show marked improvements after application of our methods. We verify our methods in simulations and provide the studyStrap R package. * NSF-DMS1810829 † T32 AI 007358 ‡ NIH, R01 DA048096; NIH, R01 MH121099; NIH, R01 NS092701; NIH, 5KL2TR00142 § WFSOM, Phys/Pharm Neurosurgery

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Section: Methodsmentioning

confidence: 99%

Section: 2mentioning

confidence: 99%

Hierachical Resampling for Bagging in Multi-Study Prediction with Applications to Human Neurochemical Sensing

Loewinger

Patil

Kishida

et al. 2019

Preprint

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“…The Early Drift Detection Method (EDDM) [112] is a further development of DDM; its drift detection is based on estimating the distribution of distances between classification errors, however EDDM is very sensitive to noise. A more recent concept drift detection method tailored for machine learning uses statistics about the extent to which models are modified by newly arriving data [113].…”

Section: ) Adapting Batch Data Mining Techniques To Analyse Data Strmentioning

confidence: 99%

Mapping the Big Data Landscape: Technologies, Platforms and Paradigms for Real-Time Analytics of Data Streams

2021

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“…Identifying the concept drift is far from trivial and is out of the scope of this article. However, several solutions are being proposed to detect the concept drift 71,72 that can be coupled to SpaCE in the future to determine when the model needs to be retrained.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Towards optimizing the execution of spark scientific workflows using machine learning‐based parameter tuning

Oliveira

Porto

Boeres

et al. 2020

Concurrency and Computation

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Summary In the last few years, Apache Spark has become a de facto the standard framework for big data systems on both industry and academy projects. Spark is used to execute compute‐ and data‐intensive workflows in distinct areas like biology and astronomy. Although Spark is an easy‐to‐install framework, it has more than one hundred parameters to be set, besides domain‐specific parameters of each workflow. In this way, to execute Spark‐based workflows efficiently, the user has to fine‐tune a myriad of Spark and workflow parameters (eg, partitioning strategy, the average size of a DNA sequence, etc.). This configuration task cannot be manually performed in a trial‐and‐error manner since it is tedious and error‐prone. This article proposes an approach that focuses on generating interpretable predictive machine learning models (ie, decision trees), and then extract useful rules (ie, patterns) from these models that can be applied to configure parameters of future executions of the workflow and Spark for nonexperts users. In the experiments presented in this article, the proposed parameter configuration approach led to better performance in processing Spark workflows. Finally, the approach introduced here reduced the number of parameters to be configured by identifying the most relevant domain‐specific ones related to the workflow performance in the predictive model.

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A Novel Concept Drift Detection Method for Incremental Learning in Nonstationary Environments

Cited by 78 publications

References 50 publications

Hierachical Resampling for Bagging in Multi-Study Prediction with Applications to Human Neurochemical Sensing

Hierachical Resampling for Bagging in Multi-Study Prediction with Applications to Human Neurochemical Sensing

Mapping the Big Data Landscape: Technologies, Platforms and Paradigms for Real-Time Analytics of Data Streams

Towards optimizing the execution of spark scientific workflows using machine learning‐based parameter tuning

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