Adaptive Learning from Evolving Data Streams

Bifet, Albert; Gavaldà, Ricard

doi:10.1007/978-3-642-03915-7_22

Cited by 347 publications

(234 citation statements)

References 6 publications

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“…Since the development of CVFDT, a number of modifications have been proposed [42,7]. Hoeglinger and Pears proposed an alternative to CVFDT which is based on a concept-based window, as opposed to the fixed window in CVFDT.…”

Section: Decision Tree Based Methodsmentioning

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: Decision Tree Based Methodsmentioning

confidence: 99%

“…More recently, Bifet and Gavaldà [7] proposed two new methods: the Hoeffding window tree (HWT), and the Hoeffding adaptive tree (HAT). HWT is similar to CVFDT with two major differences.…”

Section: Decision Tree Based Methodsmentioning

confidence: 99%

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

2012

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“…Batch models should be periodically retrained in order to adapt the classification model to the variations of the network traffic, which is a complex and very costly task [12]. Hoeffding Adaptive Tree (HAT), proposed in [24], solves this problem by implementing the Adaptive Sliding Window (ADWIN). This sliding window technique is able to detect changes in the stream (i.e., concept drift) and provide estimators of some important parameters of the input distribution using data saved in a limited and fixed amount of memory, which is independent of the total size of the data stream.…”

Section: Hoeffding Adaptive Treementioning

confidence: 99%

“…After the parametrization Section 6 presents an evaluation with the complete MAWI dataset. We briefly describe each parameter, however, we refer the interested reader to [24] for a detailed explanation.…”

Section: Hoeffding Adaptive Tree Parametrizationmentioning

confidence: 99%

A streaming flow-based technique for traffic classification applied to 12 + 1 years of Internet traffic

et al. 2015

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The continuous evolution of Internet traffic and its applications makes the classification of network traffic a topic far from being completely solved. An essential problem in this field is that most of proposed techniques in the literature are based on a static view of the network traffic (i.e. they build a model or a set of patterns from a static, invariable dataset). However, very little work has addressed the practical limitations that arise when facing a more realistic scenario with an infinite, continuously evolving stream of network traffic flows. In this paper, we propose a streaming flowbased classification solution based on Hoeffding Adaptive Tree, a machine learning technique specifically designed for evolving data streams. The main novelty of our proposal is that it is able to automatically adapt to the continuous evolution of the network traffic without storing any traffic data. We apply our solution to a 12+1 year-long dataset from a transit link in Japan, and show that it can sustain a very high accuracy over the years, with significantly less cost and complexity than existing alternatives based on static learning algorithms, such as C4.5.Valentín Carela-Español ( ) UPC BarcelonaTech,

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“…We use the Hoeffding Adaptive Trees proposed in [5], where a new method for managing alternate trees is proposed. The general idea is simple: we place ADWIN instances at every node that will raise an alert whenever something worth attention happens at the node.…”

Section: Adwin Bagging Using Hoeffding Adaptive Treesmentioning

confidence: 99%

Improving Adaptive Bagging Methods for Evolving Data Streams

Bifet

Holmes

Pfahringer

et al. 2009

Lecture Notes in Computer Science

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Abstract. We propose two new improvements for bagging methods on evolving data streams. Recently, two new variants of Bagging were proposed: ADWIN Bagging and Adaptive-Size Hoeffding Tree (ASHT) Bagging. ASHT Bagging uses trees of different sizes, and ADWIN Bagging uses ADWIN as a change detector to decide when to discard underperforming ensemble members. We improve ADWIN Bagging using Hoeffding Adaptive Trees, trees that can adaptively learn from data streams that change over time. To speed up the time for adapting to change of Adaptive-Size Hoeffding Tree (ASHT) Bagging, we add an error change detector for each classifier. We test our improvements by performing an evaluation study on synthetic and real-world datasets comprising up to ten million examples.

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Adaptive Learning from Evolving Data Streams

Cited by 347 publications

References 6 publications

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

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

A streaming flow-based technique for traffic classification applied to 12 + 1 years of Internet traffic

Improving Adaptive Bagging Methods for Evolving Data Streams

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