Massive Online Analysis
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
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]
- Bayesian classifiers
- Regression
- Clustering[8]
- StreamKM++
- CluStream
- ClusTree
- D-Stream
- CobWeb.
- Outlier detection[9]
- STORM
- Abstract-C
- COD
- MCOD
- AnyOut[10]
- Recommender systems
- BRISMFPredictor
- Frequent pattern mining
- 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
- 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
- "Release 20.12.0". 16 December 2020. Retrieved 13 January 2021.
- Bifet, Albert; Holmes, Geoff; Kirkby, Richard; Pfahringer, Bernhard (2010). "MOA: Massive online analysis". The Journal of Machine Learning Research. 99: 1601–1604.
- 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.
- 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.
- 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.
- 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.
- Almeida, Ezilda; Ferreira, Carlos; Gama, João (2013). "Adaptive Model Rules from Data Streams". Advanced Information Systems Engineering. Lecture Notes in Computer Science. 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.
- 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.
- 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.
- 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. 7238. pp. 228–242. doi:10.1007/978-3-642-29038-1_18. ISBN 978-3-642-29037-4. ISSN 0302-9743.
- 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.
- 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.
- 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. 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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