Effective learning in dynamic environments by explicit context tracking

Widmer, Gerhard; Kubát, Miroslav

doi:10.1007/3-540-56602-3_139

Cited by 126 publications

(83 citation statements)

References 15 publications

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“…In addition to classifying drift based on the changes in probabilities, the different forms of concept drift can be further classified as either real concept drift, or virtual concept drift [79,85]. In virtual concept drift, while the distribution of instances may change (corresponding to a change in the class priors or the distribution of the classes), the underlying concept (i.e., the posterior distribution) does not.…”

Section: Real Drift Versus Virtual Driftmentioning

confidence: 99%

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Learning from streaming data with concept drift and imbalance: an overview

2012

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The primary focus of machine learning has traditionally been on learning from data assumed to be sufficient and representative of the underlying fixed, yet unknown, distribution. Such restrictions on the problem domain paved the way for development of elegant algorithms with theoretically provable performance guarantees. As is often the case, however, real-world problems rarely fit neatly into such restricted models. For instance class distributions are often skewed, resulting in the "class imbalance" problem. Data drawn from non-stationary distributions is also common in real-world applications, resulting in the "concept drift" or "non-stationary learning" problem which is often associated with streaming data scenarios. Recently, these problems have independently experienced increased research attention, however, the combined problem of addressing all of the above mentioned issues has enjoyed relatively little research. If the ultimate goal of intelligent machine learning algorithms is to be able to address a wide spectrum of real-world scenarios, then the need for a general framework for learning from, and adapting to, a non-stationary environment that may introduce imbalanced data can be hardly overstated. In this paper, we first present an overview of each of these challenging areas, followed by a comprehensive review of recent research for developing such a general framework.

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Section: Real Drift Versus Virtual Driftmentioning

confidence: 99%

“…One of the original windowing methods is due to Kubat and Widmer [85,86] in FLORA3. FLORA3 is an extension of FLORA [53], and FLORA2 by [84].…”

Section: Windowing Techniquesmentioning

confidence: 99%

Learning from streaming data with concept drift and imbalance: an overview

2012

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“…When the streamed data are recorded over an extended period of time, the statistics in the data are likely to change. The time-dependent variation of statistics in streamed data are termed concept drift in the literature [6], [2], [7], [8], [9].…”

Section: A Tackling Concept Drifts In Time-varying Datamentioning

confidence: 99%

Migratory Logistic Regression for Learning Concept Drift Between Two Data Sets With Application to UXO Sensing

Liao

Carin

2009

IEEE Trans. Geosci. Remote Sensing

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“…-virtual concept drift means that changes do not impact the posterior probabilities, but affect the conditional probability density functions (Widmer and Kubat 1993). -real concept drift means that changes affect the posterior probabilities and may impact unconditional probability density function (Schlimmer and Granger 1986;Widmer and Kubat 1996).…”

Section: Introductionmentioning

confidence: 99%

One-class classifiers with incremental learning and forgetting for data streams with concept drift

Krawczyk

Woźniak

2014

Soft Comput

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One of the most important challenges for machine learning community is to develop efficient classifiers which are able to cope with data streams, especially with the presence of the so-called concept drift. This phenomenon is responsible for the change of classification task characteristics, and poses a challenge for the learning model to adapt itself to the current state of the environment. So there is a strong belief that one-class classification is a promising research direction for data stream analysis-it can be used for binary classification without an access to counterexamples, decomposing a multi-class data stream, outlier detection or novel class recognition. This paper reports a novel modification of weighted one-class support vector machine, adapted to the non-stationary streaming data analysis. Our proposition can deal with the gradual concept drift, as the introduced one-class classifier model can adapt its decision boundary to new, incoming data and additionally employs a forgetting mechanism which boosts the ability of the classifier to follow the model changes. In this work, we propose several different strategies for incremental learning and forgetting, and additionally we evaluate them on the basis of several real data streams. Obtained results confirmed the usability of proposed classifier to the problem of data stream classification with the presence of concept drift. Additionally, implemented

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Effective learning in dynamic environments by explicit context tracking

Cited by 126 publications

References 15 publications

Learning from streaming data with concept drift and imbalance: an overview

Learning from streaming data with concept drift and imbalance: an overview

Migratory Logistic Regression for Learning Concept Drift Between Two Data Sets With Application to UXO Sensing

One-class classifiers with incremental learning and forgetting for data streams with concept drift

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