On Classifying Drifting Concepts in P2P Networks
Publication Type
Conference Proceeding Article
Publication Date
9-2010
Abstract
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.
Keywords
Concept drift, classification, peer-to-peer (P2P) networks, distributed classification
Discipline
Computer Sciences | Databases and Information Systems
Publication
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings Part I
Volume
6321
First Page
24
Last Page
39
ISBN
9783642041808
Identifier
10.1007/978-3-642-15880-3_8
Publisher
Springer Verlag
City or Country
Berlin
Citation
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.
Available at: https://ink.library.smu.edu.sg/sis_research/2362
Additional URL
http://dx.doi.org/10.1007/978-3-642-15880-3_8