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Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
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Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
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Mining in Anticipation for Concept Change: Proactive-Reactive Prediction in Data Streams
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CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Distributed classification in peer-to-peer networks
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Dynamic integration of classifiers for handling concept drift
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Statistical Analysis and Data Mining
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Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
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ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
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ACM Transactions on Knowledge Discovery from Data (TKDD)
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Concept drift is a common challenge for many real-world data mining and knowledge discovery applications. Most of the existing studies for concept drift are based on centralized settings, and are often hard to adapt in a distributed computing environment. In this paper, we investigate a new research problem, P2P concept drift detection, which aims to effectively classify drifting concepts in P2P networks. We propose a novel P2P learning framework for concept drift classification, which includes both reactive and proactive approaches to classify the drifting concepts in a distributed manner. Our empirical study shows that the proposed technique is able to effectively detect the drifting concepts and improve the classification performance.