Software:Massive Online Analysis
From HandWiki
| Developer(s) | University of Waikato |
|---|---|
| Operating system | Cross-platform |
| Type | Machine Learning |
| License | GNU General Public License |
| Website | moa |
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.[1]
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[2]
- Probabilistic Adaptive Windowing
- Multi-label classifiers[3]
- Active learning classifiers [4]
- Bayesian classifiers
- Regression
- Clustering[7]
- StreamKM++
- CluStream
- ClusTree
- D-Stream
- CobWeb.
- Outlier detection[8]
- STORM
- Abstract-C
- COD
- MCOD
- AnyOut[9]
- Recommender systems
- BRISMFPredictor
- Frequent pattern mining
- Change detection algorithms[12]
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
- ↑ 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.
- ↑ 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.
- ↑ 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.
- ↑ Ikonomovska, Elena; Gama, João; Džeroski, Sašo (2010). "Learning model trees from evolving data streams". Data Mining and Knowledge Discovery 23 (1): 128–168. doi:10.1007/s10618-010-0201-y. ISSN 1384-5810. http://repositorio.inesctec.pt/bitstream/123456789/2929/1/PS-08759.pdf.
- ↑ 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. doi:10.1007/978-3-642-40988-2_31. ISBN 978-3-642-38708-1.
- ↑ 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.
- ↑ 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. pp. 1061. doi:10.1145/2463676.2463691. ISBN 9781450320375.
- ↑ 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.
- ↑ 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. pp. 591. doi:10.1145/2020408.2020501. ISBN 9781450308137.
- ↑ 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.
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