On Classifying Drifting Concepts in P2P Networks
Conference Proceeding Article
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.
Concept drift, classification, peer-to-peer (P2P) networks, distributed classification
Computer Sciences | Databases and Information Systems
Data Management and Analytics
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings Part I
City or Country
ANG, Hock Hee; Gopalkrishnan, Vivekanand; NG, Wee Keong; and HOI, Steven C. H..
On Classifying Drifting Concepts in P2P Networks. (2010). Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings Part I. 6321, 24-39. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2362