MOA Massive Online Analysishttp://mloss.orgUpdates and additions to MOA Massive Online AnalysisenFri, 04 Apr 2014 03:50:20 -0000MOA Massive Online Analysis Nov-13<html><p>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: </p> <pre><code>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] </code></pre><p>These algorithms are designed for large scale machine learning, dealing with concept drift, and big data streams in real time. </p> <p>MOA supports bi-directional interaction with Weka (machine learning). MOA is free software released under the GNU GPL. </p> <p>[1] Bifet, Albert; Holmes, Geoff; Kirkby, Richard; Pfahringer, Bernhard (2010). "MOA: Massive online analysis". The Journal of Machine Learning Research 99: 1601–1604. </p> <p>[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. </p> <p>[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. </p> <p>[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. </p> <p>[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. </p> <p>[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. </p> <p>[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. </p> <p>[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. </p> <p>[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. </p> <p>[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. </p> <p>[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. </p></html>Albert Bifet,Geoff Holmes,Richard Kirkby,Bernhard PfahringerFri, 04 Apr 2014 03:50:20 -0000 learningboostingwekabaggingdata streamsensemble methods