Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

8-2008

Abstract

Data mining tasks in P2P are bound by issues like scalability, peer dynamism, asynchronism, and data privacy preservation. These challenges pose difficulties for deploying conventional machine learning techniques in P2P networks, which may be hard to achieve classification accuracies comparable to regular centralized solutions. We recently investigated the classification problem in P2P networks and proposed a novel P2P classification approach by cascading Reduced Support Vector Machines (RSVM). Although promising results were obtained, the existing solution has some drawback of redundancy in both communication and computation. In this paper, we present a new approach to over the limitation of the previous approach. The new method can effectively reduce the redundancy and thus significantly improve the efficiency of communication and computation, meanwhile it still maintains good classification accuracies comparable to both the centralized solution and the previously proposed P2P solution. Experimental results demonstrate the feasibility and effectiveness of the new P2P classification solution.

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

VLDB Workshop on Peer-to-Peer Computing 2008, August 23, Auckland, 23 August: Proceedings

First Page

13

Last Page

25

Publisher

VLDB Endowment

City or Country

Saratoga, CA

Copyright Owner and License

Authors

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