Classification in P2P networks with cascade support vector machines

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.