Project details for MOA Massive Online Analysis

Screenshot JMLR MOA Massive Online Analysis Nov-13

by abifet - April 4, 2014, 03:50:20 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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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:

    Bayesian classifiers
        Naive Bayes
        Naive Bayes Multinomial
    Decision trees classifiers
        Decision Stump
        Hoeffding Tree
        Hoeffding Option Tree
        Hoeffding Adaptive Tree
    Meta classifiers
        Bagging using ADWIN
        Bagging using Adaptive-Size Hoeffding Trees.
        Perceptron Stacking of Restricted Hoeffding Trees
        Leveraging Bagging
        Online Accuracy Updated Ensemble
    Function classifiers
        Stochastic gradient descent (SGD)
    Drift classifiers
    Multi-label classifiers[2]
    Active learning classifiers [3]
Outlier detection[7]
Recommender systems
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.

[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. pp. 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. pp. 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. p. 1061. doi:10.1145/2463676.2463691.

[8] Assent, Ira; Kranen, Philipp; Baldauf, Corinna; Seidl, Thomas (2012). AnyOut: Anytime Outlier Detection on Streaming Data 7238. pp. 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. p. 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. p. 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. pp. 92–103. doi:10.1007/978-3-642-41398-8_9. ISSN 0302-9743.

Changes to previous version:

New version November 2013

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
Supported Operating Systems: Cygwin, Linux, Macosx, Windows
Data Formats: Arff
Tags: Classification, Online Learning, Boosting, Weka, Bagging, Data Streams, Ensemble Methods
Archive: download here

Other available revisons

Version Changelog Date

New version November 2013

April 4, 2014, 03:50:20

Initial Announcement on

May 3, 2010, 04:20:13


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