Massive Online Analysis

MOA

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]
• Regression
  • FIMTDD^[4]
  • AMRules^[5]
• Clustering^[6]
  • StreamKM++
  • CluStream
  • ClusTree
  • D-Stream
  • CobWeb.
• Outlier detection^[7]
  • STORM
  • Abstract-C
  • COD
  • MCOD
  • AnyOut^[8]
• Recommender systems
  • BRISMFPredictor
• Frequent pattern mining
  • Itemsets^[9]
  • Graphs^[10]
• 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

• 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

1. Bifet, Albert; Holmes, Geoff; Kirkby, Richard; Pfahringer, Bernhard (2010). "MOA: Massive online analysis". The Journal of Machine Learning Research 99: 1601–1604.
2. 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.
3. 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.
4. 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.
5. Almeida, Ezilda; Ferreira, Carlos; Gama, João (2013). "Adaptive Model Rules from Data Streams" 8188: 480–492. doi:10.1007/978-3-642-40988-2_31. ISSN 0302-9743.
6. 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": 1400–1403. doi:10.1109/ICDMW.2010.17.
7. Georgiadis, Dimitrios; Kontaki, Maria; Gounaris, Anastasios; Papadopoulos, Apostolos N.; Tsichlas, Kostas; Manolopoulos, Yannis (2013). "Continuous outlier detection in data streams": 1061. doi:10.1145/2463676.2463691.
8. Assent, Ira; Kranen, Philipp; Baldauf, Corinna; Seidl, Thomas (2012). "AnyOut: Anytime Outlier Detection on Streaming Data" 7238: 228–242. doi:10.1007/978-3-642-29038-1_18. ISSN 0302-9743.
9. Quadrana, Massimo; Bifet, Albert; Gavaldà, Ricard (2013). "An Efficient Closed Frequent Itemset Miner for the MOA Stream Mining System": 203. doi:10.3233/978-1-61499-320-9-203.
10. Bifet, Albert; Holmes, Geoff; Pfahringer, Bernhard; Gavaldà, Ricard (2011). "Mining frequent closed graphs on evolving data streams": 591. doi:10.1145/2020408.2020501.
11. Bifet, Albert; Read, Jesse; Pfahringer, Bernhard; Holmes, Geoff; Žliobaitė, Indrė (2013). "CD-MOA: Change Detection Framework for Massive Online Analysis" 8207: 92–103. doi:10.1007/978-3-642-41398-8_9. ISSN 0302-9743.

External links

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