Title

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

Research Areas

Data Management and Analytics

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

Additional URL

http://dx.doi.org/10.1007/978-3-642-15880-3_8