A drift detection method based on dynamic classifier selection

Pinage, Frederico S.; Santos, Eulanda M. dos; Gama, João

doi:10.1007/s10618-019-00656-w

Cited by 42 publications

(22 citation statements)

References 24 publications

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“…With the extensive application of ensemble approaches, it has become an important issue for designing a more efficient ensemble classification algorithm. Compared with static ensemble algorithms, dynamic selection ensemble algorithms [ 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 ] have been shown to effectively improve the F-measure and G-mean values. A dynamic selection ensemble algorithm predicts the label of the test sample by evaluating the capability level of each classifier and selects the set of the most capable or competitive classifiers.…”

Section: Ensemble Approaches For Imbalanced Classificationmentioning

confidence: 99%

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Zhao

Wang

et al. 2021

Entropy

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Imbalance ensemble classification is one of the most essential and practical strategies for improving decision performance in data analysis. There is a growing body of literature about ensemble techniques for imbalance learning in recent years, the various extensions of imbalanced classification methods were established from different points of view. The present study is initiated in an attempt to review the state-of-the-art ensemble classification algorithms for dealing with imbalanced datasets, offering a comprehensive analysis for incorporating the dynamic selection of base classifiers in classification. By conducting 14 existing ensemble algorithms incorporating a dynamic selection on 56 datasets, the experimental results reveal that the classical algorithm with a dynamic selection strategy deliver a practical way to improve the classification performance for both a binary class and multi-class imbalanced datasets. In addition, by combining patch learning with a dynamic selection ensemble classification, a patch-ensemble classification method is designed, which utilizes the misclassified samples to train patch classifiers for increasing the diversity of base classifiers. The experiments’ results indicate that the designed method has a certain potential for the performance of multi-class imbalanced classification.

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Section: Ensemble Approaches For Imbalanced Classificationmentioning

confidence: 99%

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Zhao

Wang

et al. 2021

Entropy

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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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“…Some methods [ 9 , 22 ] gradually update statistical model without explicit detection of concept drift. Other studies attempt to detect concept drift in batch-based methods [ 23 , 24 , 25 , 26 ] and online methods [ 27 , 28 , 29 ]. While [ 30 , 31 ] rely on process control, detects concept drift using a multiple-window-based method like [ 32 ].…”

Section: Related Workmentioning

confidence: 99%

FlexSketch: Estimation of Probability Density for Stationary and Non-Stationary Data Streams

Park

Kim

2021

Sensors

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Efficient and accurate estimation of the probability distribution of a data stream is an important problem in many sensor systems. It is especially challenging when the data stream is non-stationary, i.e., its probability distribution changes over time. Statistical models for non-stationary data streams demand agile adaptation for concept drift while tolerating temporal fluctuations. To this end, a statistical model needs to forget old data samples and to detect concept drift swiftly. In this paper, we propose FlexSketch, an online probability density estimation algorithm for data streams. Our algorithm uses an ensemble of histograms, each of which represents a different length of data history. FlexSketch updates each histogram for a new data sample and generates probability distribution by combining the ensemble of histograms while monitoring discrepancy between recent data and existing models periodically . When it detects concept drift, a new histogram is added to the ensemble and the oldest histogram is removed. This allows us to estimate the probability density function with high update speed and high accuracy using only limited memory. Experimental results demonstrate that our algorithm shows improved speed and accuracy compared to existing methods for both stationary and non-stationary data streams.

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A drift detection method based on dynamic classifier selection

Cited by 42 publications

References 24 publications

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

An overview of unsupervised drift detection methods

FlexSketch: Estimation of Probability Density for Stationary and Non-Stationary Data Streams

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