This repository contains all implementations and experiments on Volatility Adpative Classifier developed by Ruolin Jia.
Volatility Adpative Classifier is an algoirhtm used in mining data stream in which the volatility drifts occur regularly.
All implementations and experienments run in MOA. See below for more information about MOA
MOA is the most popular open source framework for data stream mining, with a very active growing community (blog). It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation. Related to the WEKA project, MOA is also written in Java, while scaling to more demanding problems.
MOA performs BIG DATA stream mining in real time, and large scale machine learning. MOA can be extended with new mining algorithms, and new stream generators or evaluation measures. The goal is to provide a benchmark suite for the stream mining community.
- MOA users: http://groups.google.com/group/moa-users
- MOA developers: http://groups.google.com/group/moa-development
If you want to refer to MOA in a publication, please cite the following JMLR paper:
Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer (2010); MOA: Massive Online Analysis; Journal of Machine Learning Research 11: 1601-1604