Massive Online Analysis
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| Developer(s) | University of Waikato |
|---|---|
| Stable release | 2014.11 / 2014/11/30 |
| Operating system | Cross-platform |
| Type | Machine Learning |
| License | GNU General Public License |
| Website | http://moa.cms.waikato.ac.nz/ |
MOA (Massive Online Analysis)[1] is a free open-source software specific for Data stream mining with Concept drift. It is written in Java and developed at the University of Waikato, New Zealand.
Description
MOA is an open-source framework software that allows to build and run experiments of machine learning or data mining on evolving data streams. It includes a set of learners and stream generators that can be used from the Graphical User Interface (GUI), the command-line, and the Java API. MOA contains several collections of machine learning algorithms:
- Classification
- Bayesian classifiers
- Naive Bayes
- Naive Bayes Multinomial
- Decision trees classifiers
- Decision Stump
- Hoeffding Tree
- Hoeffding Option Tree
- Hoeffding Adaptive Tree
- Meta classifiers
- Bagging
- Boosting
- Bagging using ADWIN
- Bagging using Adaptive-Size Hoeffding Trees.
- Perceptron Stacking of Restricted Hoeffding Trees
- Leveraging Bagging
- Online Accuracy Updated Ensemble
- Function classifiers
- Perceptron
- Stochastic gradient descent (SGD)
- Pegasos
- Drift classifiers
- Multi-label classifiers[2]
- Active learning classifiers [3]
- Bayesian classifiers
- Regression
- Clustering[6]
- StreamKM++
- CluStream
- ClusTree
- D-Stream
- CobWeb.
- Outlier detection[7]
- STORM
- Abstract-C
- COD
- MCOD
- AnyOut[8]
- Recommender systems
- BRISMFPredictor
- Frequent pattern mining
- Change detection algorithms[11]
These algorithms are designed for large scale machine learning, dealing with concept drift, and big data streams in real time.
MOA supports bi-directional interaction with Weka (machine learning). MOA is free software released under the GNU GPL.
See also
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- ADAMS Workflow: Workflow engine for MOA and Weka (machine learning)
- Streams: Flexible module environment for the design and execution of data stream experiments
- Weka (machine learning)
- Vowpal Wabbit
- List of numerical analysis software
References
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