Dynamic weighted majority: a new ensemble method for tracking concept drift

Kolter, J. Zico; Maloof, Marcus A.

doi:10.1109/icdm.2003.1250911

Cited by 488 publications

(633 citation statements)

References 17 publications

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“…The positive class expansion problem appears to have some relationship with PULearning [12,17], concept drift [9,10], and covariate shift [8,1]. But in fact it is very different from these tasks.…”

Section: Related Workmentioning

confidence: 99%

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A framework for modeling positive class expansion with single snapshot

Zhou

2009

Knowl Inf Syst

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Abstract. In many real-world data mining tasks, the coverage of the target concept may change as the time changes. For example,the coverage of "learned knowledge" of a student today may be different from his/er "learned knowledge" tomorrow, since the "learned knowledge" of the student is in expanding everyday. In order to learn a model capable of making accurate predictions, the evolution of the concept must be considered, and thus, a series of data sets collected at different time is needed. However, in many cases there is only a single data set instead of a series of data sets. In other words, only a single snapshot of the data along the time axis is available. In this paper, we show that for positive class expansion, i.e., the coverage of the target concept is in expanding as illustrated in the above "learned knowledge" example, we can learn an accurate model from the single snapshot data with the help of domain knowledge given by user. The effectiveness of the proposed framework is validated in experiments.

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Section: Related Workmentioning

confidence: 99%

“…A desired approach for concept drift problem is the one that correctly detects and fast adapts to the drift, e.g. [9,10].…”

Section: Related Workmentioning

confidence: 99%

A framework for modeling positive class expansion with single snapshot

Zhou

2009

Knowl Inf Syst

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“…Dynamic Weight Majority (DWM) [17] is a learning algorithm for tracking concept drift, which predicts using a weightedmajority vote of "experts", and dynamically creates and deletes experts in response to changes in performance. Our approach tracks "an agent's reputation" by consulting recommendations (votes) from peers (experts), and dynamically changes their recommendation reputation according to their prediction accuracy.…”

Section: Recommendation Managermentioning

confidence: 99%

Enhanced Reputation Mechanism for Mobile Ad Hoc Networks

Liu

Issarny

2004

Lecture Notes in Computer Science

140

104

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Abstract.Interactions between entities unknown to each other are inevitable in the ambient intelligence vision of service access anytime, anywhere. Trust management through a reputation mechanism to facilitate such interactions is recognized as a vital part of mobile ad hoc networks, which features lack of infrastructure, autonomy, mobility and resource scarcity of composing light-weight terminals. However, the design of a reputation mechanism is faced by challenges of how to enforce reputation information sharing and honest recommendation elicitation. In this paper, we present a reputation model, which incorporates two essential dimensions, time and context, along with mechanisms supporting reputation formation, evolution and propagation. By introducing the notion of recommendation reputation, our reputation mechanism shows effectiveness in distinguishing truth-telling and lying agents, obtaining true reputation of an agent, and ensuring reliability against attacks of defame and collusion.

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“…Although there is much research in the data stream literature on detecting concept drift and adapting to it over time [10,17,21], most work on stream classification assumes that data is distributed not identically, but still independently. Except for our brief technical report [24], we are not aware of any work in data stream classification discussing what effects a temporal dependence can have on evaluation.…”

Section: Introductionmentioning

confidence: 99%

“…The Electricity dataset due to [15] is a popular benchmark for testing adaptive classifiers. It has been used in over 40 concept drift experiments 5 , for instance, [10,17,6,21]. The Electricity Dataset was collected from the Australian New South Wales Electricity Market.…”

Section: Introductionmentioning

confidence: 99%

Pitfalls in Benchmarking Data Stream Classification and How to Avoid Them

Bifet

Read

Žliobaitė

et al. 2013

Lecture Notes in Computer Science

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Abstract. Data stream classification plays an important role in modern data analysis, where data arrives in a stream and needs to be mined in real time. In the data stream setting the underlying distribution from which this data comes may be changing and evolving, and so classifiers that can update themselves during operation are becoming the state-of-the-art. In this paper we show that data streams may have an important temporal component, which currently is not considered in the evaluation and benchmarking of data stream classifiers. We demonstrate how a naive classifier considering the temporal component only outperforms a lot of current state-of-the-art classifiers on real data streams that have temporal dependence, i.e. data is autocorrelated. We propose to evaluate data stream classifiers taking into account temporal dependence, and introduce a new evaluation measure, which provides a more accurate gauge of data stream classifier performance. In response to the temporal dependence issue we propose a generic wrapper for data stream classifiers, which incorporates the temporal component into the attribute space.

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Dynamic weighted majority: a new ensemble method for tracking concept drift

Cited by 488 publications

References 17 publications

A framework for modeling positive class expansion with single snapshot

A framework for modeling positive class expansion with single snapshot

Enhanced Reputation Mechanism for Mobile Ad Hoc Networks

Pitfalls in Benchmarking Data Stream Classification and How to Avoid Them

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