Accuracy Updated Ensemble for Data Streams with Concept Drift

Brzeziński, Dariusz; Stefanowski, Jerzy

doi:10.1007/978-3-642-21222-2_19

Cited by 130 publications

(103 citation statements)

References 7 publications

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“…In other words, the proposed model improves ensemble reactions when facing different drifts while decreasing influences of data chunk size on prediction accuracy. AUE2 is an enhance for AUE1 [17] with some changes in weighting and updating mechanisms to reduce computation cost and to increase accuracy of prediction. [15] for Data Streams.…”

Section: Incremental Learning and Ensemble Methodsmentioning

confidence: 99%

A review on data stream classification approaches

Homayoun

Ahmadzadeh

2016

JACST

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Stream data is usually in vast volume, changing dynamically, possibly infinite, and containing multi-dimensional features. The attention towards data stream mining is increasing as regards to its presence in wide range of real-world applications, such as e-commerce, banking, sensor data and telecommunication records. Similar to data mining, data stream mining includes classification, clustering, frequent pattern mining etc. techniques; the special focus of this paper is on classification methods invented to handle data streams. Early methods of data stream classification needed all instances to be labeled for creating classifier models, but there are some methods (SemiSupervised Learning and Active Learning) in which unlabeled data is employed as well as labeled data. In this paper, by focusing on ensemble methods, semi-supervised and active learning, a review on some state of the art researches is given.

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Section: Incremental Learning and Ensemble Methodsmentioning

confidence: 99%

A review on data stream classification approaches

Homayoun

Ahmadzadeh

2016

JACST

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“…The nine meta learning algorithms derived from MOA are: Adaptive Random Forest (ARF) Gomes et al (2017), OzaBagASHT (AS), OzaBagADWIN (OB) Bifet et al (2009), LeveragingBag (LB) Bifet et al (2010b), LimAttClassifier (LA) Bifet et al (2010a), AccuracyWeightedEnsemble (AWE) Wang et al (2003), AccuracyUpdatedEnsemble (AUE) Brzeziński & Stefanowski (2011), Anticipative Dynamic Adaptation to Concept Change (AD) Jaber et al (2013a) and Dynamic Adaptation to Concept Changes(DA) Jaber et al (2013b). (9) 49.3(10) 63.7(9) 78.5(8) 73.4(12) 65.8(5) 10 AUE 76.4 (7) 71.1(7) 72.5 (7) 64.2(7) 82.8 (7) 81.8(10) 65.1 (7) (2) 82.9(6) 85.4(7) 69.5(2) 3…”

Section: Algorithms Used In Studymentioning

confidence: 99%

The incremental Fourier classifier: Leveraging the discrete Fourier transform for classifying high speed data streams

Kithulgoda

Pears

Naeem

2018

Expert Systems with Applications

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“…In this way, most existing proposals do not exploit this aspect and have to learn new concepts from scratch even if they are recurrent as it is the case of Brzezinski and Stefanowski (2011) and Brzezinski and Stefanowski (2013) where an ensemble mechanism is used to deal with concept drift. Similarly, in Katakis et al (2010) an ensemble is also used, but incremental clustering is performed to maintain information on historical concepts.…”

Section: Data Stream Classificationmentioning

confidence: 99%

Predicting recurring concepts on data-streams by means of a meta-model and a fuzzy similarity function

Ángel

Bártolo²,

Ernestina

2016

Expert Systems with Applications

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Stream-mining approach is defined as a set of cutting-edge techniques designed to process streams of data in real time, in order to extract knowledge. In the particular case of classification, stream-mining has to adapt its behavior to the volatile underlying data distributions, what has been called concept drift. It is important to note that concept drift may lead to situations where predictive models become invalid and have therefore to be updated to represent the actual concepts that data poses. In this context, there is a specific type of concept drift, known as recurrent concept drift, where the concepts represented by data have already appeared in the past. In those cases the learning process could be saved or at least minimized by applying a previously trained model. To deal with the aforementioned scenario, meta-models can be used in the process of enhancing the drift detection mechanisms used by data stream algorithms, by representing and predicting when the change will occur. There are some real-world situations where a concept reappears, as in the case of intrusion detection systems (IDS), where the same incidents or an adaptation of them usually reappear over time. In these environments the early prediction of drift by means of a better knowledge of past models can help to anticipate to the change, thus improving efficiency of the model regarding the training instances needed. Furthermore, as a complement of meta-models, a mechanism to assess the similarity between classifica-tion models is also needed when dealing with recurrent concepts. In this context, when reusing a previously trained model a rough comparison between concepts is usually made, applying boolean logic. The intro-duction of fuzzy logic comparisons between models could lead to a better efficient reuse of previously seen concepts, by applying not just equal models, but also similar ones. This work faces the aforementioned open issues by means of the MM-PRec system, that integrates a meta-model mechanism and a fuzzy similarity function. The theoretical proposal of MM-PRec is also validated in this paper by means of different experiments using both synthetic and real datasets.

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Accuracy Updated Ensemble for Data Streams with Concept Drift

Cited by 130 publications

References 7 publications

A review on data stream classification approaches

A review on data stream classification approaches

The incremental Fourier classifier: Leveraging the discrete Fourier transform for classifying high speed data streams

Predicting recurring concepts on data-streams by means of a meta-model and a fuzzy similarity function

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