Learning in the presence of concept drift and hidden contexts

Widmer, Gerhard; Kubát, Miroslav

doi:10.1007/bf00116900

Cited by 1,136 publications

(995 citation statements)

References 28 publications

(36 reference statements)

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“…FLORA Widmer and Kubat [6], presented the FLORA family of algorithms as one of the first supervised incremental learning systems for a data stream. The initial FLORA algorithm used a fixed-size sliding window scheme.…”

Section: Learning Methods For Data Streams In Nsementioning

confidence: 99%

On the Online Classification of Data Streams Using Weak Estimators

Tavasoli

Oommen

Yazidi

2016

Trends in Applied Knowledge-Based Systems and Data Science

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Abstract. In this paper, we propose a novel online classifier for complex data streams which are generated from non-stationary stochastic properties. Instead of using a single training model and counters to keep important data statistics, the introduced online classifier scheme provides a real-time self-adjusting learning model. The learning model utilizes the multiplication-based update algorithm of the Stochastic Learning Weak Estimator (SLWE) at each time instant as a new labeled instance arrives. In this way, the data statistics are updated every time a new element is inserted, without requiring that we have to rebuild its model when changes occur in the data distributions. Finally, and most importantly, the model operates with the understanding that the correct classes of previously-classified patterns become available at a later juncture subsequent to some time instances, thus requiring us to update the training set and the training model. The results obtained from rigorous empirical analysis on multinomial distributions, is remarkable. Indeed, it demonstrates the applicability of our method on synthetic datasets, and proves the advantages of the introduced scheme.

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Section: Learning Methods For Data Streams In Nsementioning

confidence: 99%

On the Online Classification of Data Streams Using Weak Estimators

Tavasoli

Oommen

Yazidi

2016

Trends in Applied Knowledge-Based Systems and Data Science

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“…This method is efficient when changes are occasional, and stable periods quite long between those concept drifts. Some drift detectors are based on changes in the probability distribution of the data samples [15], and others on changes in the classification accuracy [16]. In our case, this method isn't appropriate since we not only want our system to adapt to abrupt concept drifts, but also to smoothly follow slow concept shifts.…”

Section: Online Classification and Evolving Systemsmentioning

confidence: 99%

Online active supervision of an evolving classifier for customized-gesture-command learning

Bouillon

Anquetil

2017

Neurocomputing

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To cite this version:Manuel Bouillon, Eric Anquetil.Online active supervision of an evolving classifier for customized-gesture-command learning.Neurocomputing AbstractTouch sensitive interfaces enable new interaction methods, like gesture commands. For users to easily memorize more than a dozen of gesture commands, it is important to enable gesture set customization. The classifier used to recognize drawn symbols must hence be customizable -able to learn from very few data -and evolving -able to learn new classes on-the-fly and improve during its use. The objective of this work is to obtain a gesture command system that cooperates as best as possible with the user: that learns from its mistakes without soliciting the user too often. This paper presents a novel approach for the online active learning of gesture commands, with three contributions. The IntuiSup supervisor monitors the learning process and user interactions. The Evolving Sampling by Uncertainty (ESU) algorithm enables to maintain the error/interaction compromise over time. The Boosted-ESU (B-ESU) method optimizes interaction impact to fasten system learning speed. The efficiency of our approach is evaluated on the publicly available ILG Data Base of gesture commands. Experimentation shows the effectiveness of the supervision strategy and improvements both in term of accuracy and learning speed.

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“…In data mining and machine learning these changes are generally known as concept drift, that is the changes in the (hidden) context inducing more or less radical changes in the target concept [11]. The challenge is to keep track of the drift and adjust the model accordingly.…”

Section: Conclusion and Further Workmentioning

confidence: 99%

Online Mass Flow Prediction in CFB Boilers

Ivannikov

Pechenizkiy

Bakker

et al. 2009

Advances in Data Mining. Applications and Theoretical Aspects

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Abstract. Fuel feeding and inhomogeneity of fuel typically cause process fluctuations in the circulating fluidized bed (CFB) process. If control systems fail to compensate for the fluctuations, the whole plant will suffer from fluctuations that are reinforced by the closed-loop controls. This phenomenon causes a reduction of efficiency and lifetime of process components. Therefore, domain experts are interested in developing tools and techniques for getting better understanding of underlying processes and their mutual dependencies in CFB boilers. In this paper we consider an application of data mining technology to the analysis of time series data from a pilot CFB reactor. Namely, we present a rather simple and intuitive approach for online mass flow prediction in CFB boilers. This approach is based on learning and switching regression models. Additionally, noise canceling, and windowing mechanisms are used for improving the robustness of online prediction. We validate our approach with a set of simulation experiments with real data collected from the pilot CFB boiler.

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Learning in the presence of concept drift and hidden contexts

Cited by 1,136 publications

References 28 publications

On the Online Classification of Data Streams Using Weak Estimators

On the Online Classification of Data Streams Using Weak Estimators

Online active supervision of an evolving classifier for customized-gesture-command learning

Online Mass Flow Prediction in CFB Boilers

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