Conference Proceeding Article
Distributed classification aims to build an accurate classifier by learning from distributed data while reducing computation and communication cost A P2P network where numerous users come together to share resources like data content, bandwidth, storage space and CPU resources is an excellent platform for distributed classification However, two important aspects of the learning environment have often been overlooked by other works, viz., 1) location of the peers which results in variable communication cost and 2) heterogeneity of the peers' data which can help reduce redundant communication In this paper, we examine the properties of network and data heterogeneity and propose a simple yet efficient P2P classification approach that minimizes expensive inter-region communication while achieving good generalization performance Experimental results demonstrate the feasibility and effectiveness of the proposed solution.
Computer Sciences | Databases and Information Systems
Data Management and Analytics
Advances in Knowledge Discovery and Data Mining: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings. Part II
City or Country
ANG, Hock Kee; Gopalkrishnan, Vivekanand; DATTA, Anwitaman; NG, Wee Keong; and HOI, Steven C. H..
Satrap: Data and Network Heterogeneity Aware P2P Data-mining. (2010). Advances in Knowledge Discovery and Data Mining: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings. Part II. 63-70. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2366
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