Algorithm Selection on Data Streams

Rijn, Jan N. van; Holmes, Geoffrey; Pfahringer, Bernhard; Vanschoren, Joaquin

doi:10.1007/978-3-319-11812-3_28

Cited by 45 publications

(37 citation statements)

References 17 publications

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“…Few concepts for automated algorithm selection on streaming data exist so far, both for supervised (see, e.g., van Rijn et al, 2014van Rijn et al, , 2018 and unsupervised learning algorithms. In unsupervised learning, stream clustering is a very active research field.…”

Section: Performance Measuresmentioning

confidence: 99%

Automated Algorithm Selection: Survey and Perspectives

Kerschke

Hoos

Neumann

et al. 2019

Evolutionary Computation

317

184

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It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronounced for computationally hard problems, where in most cases, no single algorithm defines the state of the art; instead, there is a set of algorithms with complementary strengths. This performance complementarity can be exploited in various ways, one of which is based on the idea of selecting, from a set of given algorithms, for each problem instance to be solved the one expected to perform best. The task of automatically selecting an algorithm from a given set is known as the per-instance algorithm selection problem and has been intensely studied over the past 15 years, leading to major improvements in the state of the art in solving a growing number of discrete combinatorial problems, including propositional satisfiability and AI planning. Perinstance algorithm selection also shows much promise for boosting performance in solving continuous and mixed discrete/continuous optimisation problems.This survey provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas. Different from earlier work, it covers applications to discrete and continuous problems, and discusses algorithm selection in context with conceptually related approaches, such as algorithm configuration, scheduling or portfolio selection. Since informative and cheaply computable problem instance features provide the basis for effective perinstance algorithm selection systems, we also provide an overview of such features for discrete and continuous problems. Finally, we provide perspectives on future work in the area and discuss a number of open research challenges.

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Section: Performance Measuresmentioning

confidence: 99%

Automated Algorithm Selection: Survey and Perspectives

Kerschke

Hoos

Neumann

et al. 2019

Evolutionary Computation

317

184

View full text Add to dashboard Cite

show abstract

“…However, an initial approach on configuring and benchmarking stream clustering approaches based on irace (López-Ibáñez et al 2016) has been presented by Carnein et al (2017a). Very promising are ensemblebased approaches, both for algorithm selection and configuration on data streams, which have successfully been applied in the context of classification algorithms already (van Rijn et al 2014(van Rijn et al , 2018.…”

Section: Algorithm Configurationmentioning

confidence: 99%

Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms

Carnein

Trautmann

2019

Bus Inf Syst Eng

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Analyzing data streams has received considerable attention over the past decades due to the widespread usage of sensors, social media and other streaming data sources. A core research area in this field is stream clustering which aims to recognize patterns in an unordered, infinite and evolving stream of observations. Clustering can be a crucial support in decision making, since it aims for an optimized aggregated representation of a continuous data stream over time and allows to identify patterns in large and high-dimensional data. A multitude of algorithms and approaches has been developed that are able to find and maintain clusters over time in the challenging streaming scenario. This survey explores, summarizes and categorizes a total of 51 stream clustering algorithms and identifies core research threads over the past decades. In particular, it identifies categories of algorithms based on distance thresholds, density grids and statistical models as well as algorithms for high dimensional data. Furthermore, it discusses applications scenarios, available software and how to configure stream clustering algorithms. This survey is considerably more extensive than comparable studies, more up-to-date and highlights how concepts are interrelated and have been developed over time.

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“…The authors of [4] dynamically choose between two regression techniques using meta-knowledge obtained earlier in the stream. The authors of [3] select the best classifier among multiple classifiers, based on meta-knowledge from previously processed data streams. Finally, [22] uses meta-learning on time series with recurrent concepts: when concept drift is detected, a metalearning algorithm decides whether a model trained previously on the same stream could be reused, or whether the data is so different from before that a new model must be trained.…”

Section: Related Workmentioning

confidence: 99%

“…As the underlying techniques upon which Stacking relies can not be trivially transferred to the data stream setting, there are few successful heterogeneous ensemble techniques in the data stream setting. Most approaches rely on meta-learning [3], [4]. This requires the extraction of computationally expensive meta-features, yet it yields marginal improvements.…”

Section: Introductionmentioning

confidence: 99%

Having a Blast: Meta-Learning and Heterogeneous Ensembles for Data Streams

Rijn

Holmes

Pfahringer

et al. 2015

2015 IEEE International Conference on Data Mining

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Ensembles of classifiers are among the best performing classifiers available in many data mining applications. However, most ensembles developed specifically for the dynamic data stream setting rely on only one type of base-level classifier, most often Hoeffding Trees. In this paper, we study the use of heterogeneous ensembles, comprised of fundamentally different model types. Heterogeneous ensembles have proven successful in the classical batch data setting, however they do not easily transfer to the data stream setting. We therefore introduce the Online Performance Estimation framework, which can be used in data stream ensembles to weight the votes of (heterogeneous) ensemble members differently across the stream. Experiments over a wide range of data streams show performance that is competitive with state of the art ensemble techniques, including Online Bagging and Leveraging Bagging. All experimental results from this work are easily reproducible and publicly available on OpenML for further analysis.

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Algorithm Selection on Data Streams

Cited by 45 publications

References 17 publications

Automated Algorithm Selection: Survey and Perspectives

Automated Algorithm Selection: Survey and Perspectives

Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms

Having a Blast: Meta-Learning and Heterogeneous Ensembles for Data Streams

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