On learning guarantees to unsupervised concept drift detection on data streams

Mello, Rodrigo Fernandes de; Vaz, Yule; Grossi, Carlos H.; Bifet, Albert

doi:10.1016/j.eswa.2018.08.054

Cited by 54 publications

(24 citation statements)

References 21 publications

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“…Online Fixed reference window IKS-bdd* dos Reis, Flach, Matwin, and Batista (2016) CD-TDS Koh (2016) OMV-PHT * Lughofer, Weigl, Heidl, Eitzinger, and Radauer (2016) NM-DDM* Mustafa et al (2017) Sliding reference window Plover de Mello, Vaz, Grossi, and Bifet (2019) SAND Haque, Khan, and Baron (2016) DSDD * Pinagé, dos Santos, and Gama (2019) Multiple approaches DbDDA* Kim and Park (2017) Note: Names with an asterisk were introduced in this survey because the methods were not given any name in the referenced works.…”

Section: Subcategory Methods Referencesmentioning

confidence: 99%

An overview of unsupervised drift detection methods

Gemaque

Costa

Giusti

et al. 2020

WIREs Data Min & Knowl

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Practical applications involving big data, such as weather monitoring, identification of customer preferences, Internet log analysis, and sensors warnings require challenging data analysis, since these are examples of problems whose data are generated in streams and usually demand real-time analytics. Patterns in such data stream problems may change quickly. Consequently, machine learning models that operate in this context must be updated over time. This phenomenon is called concept drift in machine learning and data mining literature. Several different directions have been pursued to learn from data stream and to deal with concept drift. However, most drift detection methods consider that an instance's class label is available right after its prediction, since these methods work by monitoring the prediction results of a base classifier or an ensemble of classifiers. Nevertheless, this constraint is unrealistic in several practical problems. To cope with this constraint, some works are focused on proposing efficient unsupervised or semi-supervised concept drift detectors. While interesting and recent overview papers dedicated to supervised drift detectors have been published, the scenario is not the same in terms of unsupervised methods. Therefore, this work presents a comprehensive overview of approaches that tackle concept drift in classification problems in an unsupervised manner. Additional contribution includes a proposed taxonomy of state-of-the-art approaches for concept drift detection based on unsupervised strategies.

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Section: Subcategory Methods Referencesmentioning

confidence: 99%

An overview of unsupervised drift detection methods

Gemaque

Costa

Giusti

et al. 2020

WIREs Data Min & Knowl

View full text Add to dashboard Cite

show abstract

“…In [11], the authors proposed to computing multiple model explanations over time and observing the magnitudes of their changes. The application of unsupervised methods, motivated by the statistical learning theory, are investigated in [37].…”

Section: Related Workmentioning

confidence: 99%

On Training Deep Neural Networks Using a Streaming Approach

Duda

Jaworski

Cader

et al. 2019

Journal of Artificial Intelligence and Soft Computing Research

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In recent years, many deep learning methods, allowed for a significant improvement of systems based on artificial intelligence methods. Their effectiveness results from an ability to analyze large labeled datasets. The price for such high accuracy is the long training time, necessary to process such large amounts of data. On the other hand, along with the increase in the number of collected data, the field of data stream analysis was developed. It enables to process data immediately, with no need to store them. In this work, we decided to take advantage of the benefits of data streaming in order to accelerate the training of deep neural networks. The work includes an analysis of two approaches to network learning, presented on the background of traditional stochastic and batch-based methods.

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“…This research is primarily concerned with supervised data stream mining and drift detection methods. Literature and techniques involving unsupervised approaches, such as Sethi and Kantardzic [64] and de Mello et al [23], are not reviewed. Unlike existing reviews such as Gama et al [36], this paper reviews modern, recent approaches to handling concept drift, as well as established methods covered in earlier reviews.…”

Section: Introductionmentioning

confidence: 99%

Data stream mining: methods and challenges for handling concept drift

2019

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Mining and analysing streaming data is crucial for many applications, and this area of research has gained extensive attention over the past decade. However, there are several inherent problems that continue to challenge the hardware and the state-of-the art algorithmic solutions. Examples of such problems include the unbound size, varying speed and unknown data characteristics of arriving instances from a data stream. The aim of this research is to portray key challenges faced by algorithmic solutions for stream mining, particularly focusing on the prevalent issue of concept drift. A comprehensive discussion of concept drift and its inherent data challenges in the context of stream mining is presented, as is a critical, in-depth review of relevant literature. Current issues with the evaluative procedure for concept drift detectors is also explored, highlighting problems such as a lack of established base datasets and the impact of temporal dependence on concept drift detection. By exposing gaps in the current literature, this study suggests recommendations for future research which should aid in the progression of stream mining and concept drift detection algorithms.

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On learning guarantees to unsupervised concept drift detection on data streams

Cited by 54 publications

References 21 publications

An overview of unsupervised drift detection methods

An overview of unsupervised drift detection methods

On Training Deep Neural Networks Using a Streaming Approach

Data stream mining: methods and challenges for handling concept drift

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