Massive Online Analysis

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|

MOADeveloper(s)| University of Waikato

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Stable release|

24.07.0[1] / 18 July 2024; 14 days ago (18 July 2024)

Repository|

* github.com/waikato/moa

Operating system| Cross-platform
Type| Machine Learning
License| GNU General Public License
Website| moa.cms.waikato.ac.nz

Massive Online Analysis (MOA) is a free open-source software project specific for data stream mining with concept drift. It is written in Java and developed at the University of Waikato, New Zealand.[2]

## Description

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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
* Self-Adjusting Memory[3]
* Probabilistic Adaptive Windowing
* Multi-label classifiers[4]
* Active learning classifiers [5]
* Regression
* FIMTDD[6]
* AMRules[7]
* Clustering[8]
* StreamKM++
* CluStream
* ClusTree
* D-Stream
* CobWeb.
* Outlier detection[9]
* STORM
* Abstract-C
* COD
* MCOD
* AnyOut[10]
* Recommender systems
* BRISMFPredictor
* Frequent pattern mining
* Itemsets[11]
* Graphs[12]
* Change detection algorithms[13]

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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* Free and open-source software portal

* 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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1. ^ "Release 24.07.0". 18 July 2024. Retrieved 23 July 2024.
2. ^ Bifet, Albert; Holmes, Geoff; Kirkby, Richard; Pfahringer, Bernhard (2010). "MOA: Massive online analysis". The Journal of Machine Learning Research. 99: 1601–1604.
3. ^ Losing, Viktor; Hammer, Barbara; Wersing, Heiko (2017). "Tackling heterogeneous concept drift with the Self-Adjusting Memory (SAM)". Knowledge and Information Systems. 54: 171–201. doi:10.1007/s10115-017-1137-y. ISSN 0885-6125. S2CID 29600755.
4. ^ Read, Jesse; Bifet, Albert; Holmes, Geoff; Pfahringer, Bernhard (2012). "Scalable and efficient multi-label classification for evolving data streams". Machine Learning. 88 (1–2): 243–272. doi:10.1007/s10994-012-5279-6. ISSN 0885-6125. S2CID 14676146.
5. ^ Zliobaite, Indre; Bifet, Albert; Pfahringer, Bernhard; Holmes, Geoffrey (2014). "Active Learning With Drifting Streaming Data". IEEE Transactions on Neural Networks and Learning Systems. 25 (1): 27–39. doi:10.1109/TNNLS.2012.2236570. ISSN 2162-237X. PMID 24806642. S2CID 14687075.
6. ^ Ikonomovska, Elena; Gama, João; Džeroski, Sašo (2010). "Learning model trees from evolving data streams" (PDF). Data Mining and Knowledge Discovery. 23 (1): 128–168. doi:10.1007/s10618-010-0201-y. ISSN 1384-5810. S2CID 7114108.
7. ^ Almeida, Ezilda; Ferreira, Carlos; Gama, João (2013). "Adaptive Model Rules from Data Streams". Advanced Information Systems Engineering. Lecture Notes in Computer Science. Vol. 8188\. pp. 480–492. CiteSeerX 10.1.1.638.5472. doi:10.1007/978-3-642-40988-2_31. ISBN 978-3-642-38708-1. ISSN 0302-9743.
8. ^ Kranen, Philipp; Kremer, Hardy; Jansen, Timm; Seidl, Thomas; Bifet, Albert; Holmes, Geoff; Pfahringer, Bernhard (2010). "Clustering Performance on Evolving Data Streams: Assessing Algorithms and Evaluation Measures within MOA". 2010 IEEE International Conference on Data Mining Workshops. pp. 1400–1403. doi:10.1109/ICDMW.2010.17. ISBN 978-1-4244-9244-2. S2CID 2064336.
9. ^ Georgiadis, Dimitrios; Kontaki, Maria; Gounaris, Anastasios; Papadopoulos, Apostolos N.; Tsichlas, Kostas; Manolopoulos, Yannis (2013). "Continuous outlier detection in data streams". Proceedings of the 2013 international conference on Management of data - SIGMOD '13. p. 1061\. doi:10.1145/2463676.2463691. ISBN 9781450320375. S2CID 1886134.
10. ^ Assent, Ira; Kranen, Philipp; Baldauf, Corinna; Seidl, Thomas (2012). "AnyOut: Anytime Outlier Detection on Streaming Data". Database Systems for Advanced Applications. Lecture Notes in Computer Science. Vol. 7238\. pp. 228–242. doi:10.1007/978-3-642-29038-1_18. ISBN 978-3-642-29037-4. ISSN 0302-9743.
11. ^ Quadrana, Massimo; Bifet, Albert; Gavaldà, Ricard (2013). "An Efficient Closed Frequent Itemset Miner for the MOA Stream Mining System". Frontiers in Artificial Intelligence and Applications. 256 (Artificial Intelligence Research and Development): 203. doi:10.3233/978-1-61499-320-9-203.
12. ^ Bifet, Albert; Holmes, Geoff; Pfahringer, Bernhard; Gavaldà, Ricard (2011). "Mining frequent closed graphs on evolving data streams". Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '11. p. 591\. CiteSeerX 10.1.1.297.1721. doi:10.1145/2020408.2020501. ISBN 9781450308137. S2CID 8588858.
13. ^ Bifet, Albert; Read, Jesse; Pfahringer, Bernhard; Holmes, Geoff; Žliobaitė, Indrė (2013). "CD-MOA: Change Detection Framework for Massive Online Analysis". Advances in Intelligent Data Analysis XII. Lecture Notes in Computer Science. Vol. 8207\. pp. 92–103. doi:10.1007/978-3-642-41398-8_9. ISBN 978-3-642-41397-1. ISSN 0302-9743.

## External links

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* MOA Project home page at University of Waikato in New Zealand
* SAMOA Project home page at Yahoo Labs