On Classifying Drifting Concepts in P2P Networks
Abstract
Concept drift is a common challenge for many real-world data mining and knowledge discovery applications. Most of the existing studies for concept drift are based on centralized settings, and are often hard to adapt in a distributed computing environment. In this paper, we investigate a new research problem, P2P concept drift detection, which aims to effectively classify drifting concepts in P2P networks. We propose a novel P2P learning framework for concept drift classification, which includes both reactive and proactive approaches to classify the drifting concepts in a distributed manner. Our empirical study shows that the proposed technique is able to effectively detect the drifting concepts and improve the classification performance.
Cite
Text
Ang et al. "On Classifying Drifting Concepts in P2P Networks." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010. doi:10.1007/978-3-642-15880-3_8Markdown
[Ang et al. "On Classifying Drifting Concepts in P2P Networks." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010.](https://mlanthology.org/ecmlpkdd/2010/ang2010ecmlpkdd-classifying/) doi:10.1007/978-3-642-15880-3_8BibTeX
@inproceedings{ang2010ecmlpkdd-classifying,
title = {{On Classifying Drifting Concepts in P2P Networks}},
author = {Ang, Hock Hee and Gopalkrishnan, Vivekanand and Ng, Wee Keong and Hoi, Steven C. H.},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2010},
pages = {24-39},
doi = {10.1007/978-3-642-15880-3_8},
url = {https://mlanthology.org/ecmlpkdd/2010/ang2010ecmlpkdd-classifying/}
}