Machine Learning
Boosting Algorithms for Parallel and Distributed Learning
Distributed and Parallel Databases - Special issue: Parallel and distributed data mining
Distributed Pasting of Small Votes
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Distributed Data Mining in Peer-to-Peer Networks
IEEE Internet Computing
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
An efficient update propagation algorithm for P2P systems
Computer Communications
Distributed classification in peer-to-peer networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Distributed Decision-Tree Induction in Peer-to-Peer Systems
Statistical Analysis and Data Mining
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
Cascade RSVM in Peer-to-Peer Networks
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Communication-Efficient Classification in P2P Networks
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Issues in stacked generalization
Journal of Artificial Intelligence Research
On classifying drifting concepts in P2P networks
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Adaptive ensemble classification in p2p networks
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
Automatic document organization in a p2p environment
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
A study on reduced support vector machines
IEEE Transactions on Neural Networks
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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.