Publication Type
Journal Article
Version
publishedVersion
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
Citation
ANG, Hock Hee; Gopalkrishnan, Vivekanand; HOI, Steven C. H.; and NG, Wee-Keong.
Classification in P2P Networks with Cascade Support Vendor Machines. (2013). ACM Transactions on Knowledge Discovery from Data. 7, (4), 20-1-29.
Available at: https://ink.library.smu.edu.sg/sis_research/2267
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/2541268.2541273