On Classifying Drifting Concepts in P2P Networks

Ang, Hock Hee; Gopalkrishnan, Vivekanand; Ng, Wee Keong; Hoi, Steven C. H.

doi:10.1007/978-3-642-15880-3_8

Cited by 14 publications

(20 citation statements)

References 17 publications

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“…Another solution was proposed by Ang et al [20]. This method implements the so-called RePCoDE framework which detects the drift (reactive behavior) and simultaneously predicts it as well (proactive behavior).…”

Section: Related Workmentioning

confidence: 99%

Gossip-Based Learning under Drifting Concepts in Fully Distributed Networks

Hegedűs

Ormándi

Jelasity

2012

2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems

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Abstract-In fully distributed networks data mining is an important tool for monitoring, control, and for offering personalized services to users. The underlying data model can change as a function of time according to periodic (daily, weakly) patterns, sudden changes, or long term transformations of the environment or the system itself. For a large space of the possible models for this dynamism-when the network is very large but only a few training samples can be obtained at all nodes locally-no efficient fully distributed solution is known. Here we present an approach, that is able to follow concept drift in very large scale and fully distributed networks. The algorithm does not collect data to a central location, instead it is based on online learners taking random walks in the network. To achieve adaptivity the diversity of the learners is controlled by managing the lifespans of the models. We demonstrate through a thorough experimental analysis, that in a well specified range of feasible models of concept drift, where there is little data available locally in a large network, our algorithm outperforms known methods from related work.

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Section: Related Workmentioning

confidence: 99%

Gossip-Based Learning under Drifting Concepts in Fully Distributed Networks

Hegedűs

Ormándi

Jelasity

2012

2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems

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“…In the past few years there has been an increasing number of proposals for P2P machine learning algorithms as well, like those in [5,6,7,34,56,77,109]. The usual assumption in these studies is that a peer has a subset of the training data on which a model can be learnt locally.…”

Section: Related Workmentioning

confidence: 99%

Applications of Support Vector-Based Learning

Ormándi¹

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PrefaceAn increasing amount of data is stored in electronic form. This phenomenon increases the need of the methods that help to access the useful pieces of information that are hidden in ocean of the collected data. That is, methods to perform automated data processing are needed. The role of machine learning is to provide methods that support this progress, i.e. it provides algorithms that can automatically discover those hidden and non-trivial patterns in data that are useful for us.In the focus of this thesis stands a special family of machine learning algorithms referred to as support vector-based learners. These algorithms are all based on the so-called maximal margin heuristic, which helps to select the final model from the suitable ones preserving the generalization ability of the model. These methods are quite general, and they have a numerous amazing properties like generalization capability or robustness to noisy data. However, to apply them to a specific task it is needed to adapt them to that particular task. This is challenging since the inappropriate adaptation can result in a drastic decrease in prediction performance or in the generalization capability; or cause a computationally infeasible situation (e.g. huge computational complexity or untreatable network load in a distributed setting).The goal of this thesis is to examine the suitability and adaptability of ii various support vector-based learning methods in numerous learning tasks and environments. During this, novel techniques and ideas, which are less frequent or maybe surprising, are investigated and discussed. The considered tasks themselves touch a wide range problems like time series forecasting, opinion mining, collaborative filtering, and the common binary classification problem. An uncommon computational environment is also concerned, namely the fully distributed environment.Róbert Ormándi, July 2013.

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“…In the past few years there have been an increasing number of proposals for P2P machine learning algorithms as well, like those in [5,6,7,28,54,77,101].…”

Section: Related Workmentioning

confidence: 99%

“…Learning in P2P systems is a growing area, some examples include [5,6,7,28,54,77,88,101]. Very few works address issues related to concept drift in a P2P network.…”

Section: Handling Concept Drift In Fully Distributed Environmentsmentioning

confidence: 99%

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Gossip-Based Machine Learning in Fully Distributed Environments

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On Classifying Drifting Concepts in P2P Networks

Cited by 14 publications

References 17 publications

Gossip-Based Learning under Drifting Concepts in Fully Distributed Networks

Gossip-Based Learning under Drifting Concepts in Fully Distributed Networks

Applications of Support Vector-Based Learning

Gossip-Based Machine Learning in Fully Distributed Environments

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