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.

[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

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