C4.5: programs for machine learning
C4.5: programs for machine learning
Fast Nearest Neighbor Search in High-Dimensional Space
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Distributed Data Mining in Peer-to-Peer Networks
IEEE Internet Computing
Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
Agent-based Service-Oriented Intelligent P2P Networks for Distributed Classification
ICHIT '06 Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 02
Distributed classification in peer-to-peer networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
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
Automatic document organization in a p2p environment
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
P2PDocTagger: content management through automated P2P collaborative tagging
Proceedings of the VLDB Endowment
P2P traffic classification using ensemble learning
Proceedings of the 5th IBM Collaborative Academia Research Exchange Workshop
Classification in P2P networks with cascade support vector machines
ACM Transactions on Knowledge Discovery from Data (TKDD)
Hi-index | 0.00 |
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.