Publication Type

Journal Article

Version

Postprint

Publication Date

11-2013

Abstract

Classification in Peer-to-Peer (P2P) networks is important to many real applications, such as distributed intrusion detection, distributed recommendation systems, and distributed antispam detection. However, it is very challenging to perform classification in P2P networks due to many practical issues, such as scalability, peer dynamism, and asynchronism. This article investigates the practical techniques of constructing Support Vector Machine (SVM) classifiers in the P2P networks. In particular, we demonstrate how to efficiently cascade SVM in a P2P network with the use of reduced SVM. In addition, we propose to fuse the concept of cascade SVM with bootstrap aggregation to effectively balance the trade-off between classification accuracy, model construction, and prediction cost. We provide theoretical insights for the proposed solutions and conduct an extensive set of empirical studies on a number of large-scale datasets. Encouraging results validate the efficacy of the proposed approach.

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

ACM Transactions on Knowledge Discovery from Data

Volume

7

Issue

4

First Page

20-1

Last Page

29

ISSN

1556-4681

Identifier

10.1145/2541268.2541273

Publisher

ACM

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

https://doi.org/10.1145/2541268.2541273

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