On Classifying Drifting Concepts in P2P Networks

• Main Authors: ANG, Hock Hee, Gopalkrishnan, Vivekanand, NG, Wee Keong, HOI, Steven C. H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2010
• Subjects: Concept drift; classification; peer-to-peer (P2P) networks; distributed classification; Computer Sciences; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/2362; http://dx.doi.org/10.1007/978-3-642-15880-3_8
• Institution: Singapore Management University

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.