Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

4-2010

Abstract

Classification in P2P networks has become an important research problem in data mining due to the popularity of P2P computing environments. This is still an open difficult research problem due to a variety of challenges, such as non-i.i.d. data distribution, skewed or disjoint class distribution, scalability, peer dynamism and asynchronism. In this paper, we present a novel P2P Adaptive Classification Ensemble (PACE) framework to perform classification in P2P networks. Unlike regular ensemble classification approaches, our new framework adapts to the test data distribution and dynamically adjusts the voting scheme by combining a subset of classifiers/peers according to the test data example. In our approach, we implement the proposed PACE solution together with the state-of-the-art linear SVM as the base classifier for scalable P2P classification. Extensive empirical studies show that the proposed PACE method is both efficient and effective in improving classification performance over regular methods under various adverse conditions.

Keywords

Peer to peer networks, Adaptive classification, Classification performance, Ensemble classification

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Database Systems for Advanced applications: 15th International conference, DASFAA 2010, Tsukuba, Japan, April 1-4, Proceedings

First Page

34

Last Page

48

ISBN

9783642120251

Identifier

10.1007/978-3-642-12026-8_5

Publisher

Springer

City or Country

Berlin

Additional URL

https://doi.org/10.1007/978-3-642-12026-8_5

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