Publications

MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering

Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl.
Journal of Machine Learning Research (JMLR) Workshop and Conference Proceedings.
Volume 11: Workshop on Applications of Pattern Analysis (2010).
(download the extended version here.)


Active Learning With Drifting Streaming Data.

Indre Zliobaite, Albert Bifet, Bernhard Pfahringer, Geoffrey Holmes. IEEE Trans. Neural Netw. Learning Syst. 25(1): 27-39 (2014)


CD-MOA: Change Detection Framework for Massive Online Analysis

Albert Bifet, Jesse Read, Bernhard Pfahringer, Geoff Holmes, Indre Zliobaite. IDA 2013: 92-103


Pitfalls in Benchmarking Data Stream Classification and How to Avoid Them

Albert Bifet, Jesse Read, Indre Zliobaite, Bernhard Pfahringer, and Geoff Holmes. In Proc European Conference on Machine Learning and Knowledge Discovery in Databases, Prague, Czech Republic, pages 465-479, 2013.


Continuous outlier detection in data streams: an extensible framework and state-of-the-art algorithms

Dimitrios Georgiadis, Maria Kontaki, Anastasios Gounaris, Apostolos N. Papadopoulos, Kostas Tsichlas, Yannis Manolopoulos. SIGMOD Conference 2013: 1061-1064.


An Efficient Closed Frequent Itemset Miner for the MOA Stream Mining System.

Massimo Quadrana, Albert Bifet, Ricard Gavaldà. CCIA 2013: 203-212


Scalable and efficient multi-label classification for evolving data streams.

Jesse Read, Albert Bifet, Geoff Holmes, Bernhard Pfahringer: Machine Learning 88(1-2): 243-272 (2012)


AnyOut: Anytime Outlier Detection on Streaming Data.

Ira Assent, Philipp Kranen, Corinna Baldauf, Thomas Seidl. DASFAA (1) 2012: 228-242


Stream Data Mining Using the MOA Framework.

Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl, Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Jesse Read. DASFAA (2) 2012: 309-313


Ensembles of Restricted Hoeffding Trees.

Albert Bifet, Eibe Frank, Geoff Holmes, Bernhard Pfahringer. ACM TIST 3(2): 30 (2012)


Batch-Incremental versus Instance-Incremental Learning in Dynamic and Evolving Data.

Jesse Read, Albert Bifet, Bernhard Pfahringer, Geoff Holmes. IDA 2012: 313-323


MOA-TweetReader: Real-Time Analysis in Twitter Streaming Data.

Albert Bifet, Geoffrey Holmes, Bernhard Pfahringer. Discovery Science 2011: 46-60


Mining Frequent Closed Graphs on Evolving Data Streams

Albert Bifet, Geoff Holmes, Bernhard Pfahringer and Ricard Gavaldà.
In 17th ACM SIGKDD Intl. Conference on Knowledge Discovery and Data Mining KDD’11.


An Effective Evaluation Measure for Clustering on Evolving Data Stream.

Hardy Kremer, Philipp Kranen, Timm Jansen, Thomas Seidl, Albert Bifet, Geoff Holmes and Bernhard Pfahringer.
In 17th ACM SIGKDD Intl. Conference on Knowledge Discovery and Data Mining KDD’11.


Active learning with evolving streaming data

Indrė Žliobaitė, Albert Bifet, Bernhard Pfahringer and Geoff Holmes.
In Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2011.


MOA: a Real-time Analytics Open Source Framework

Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Philipp Kranen, Hardy Kremer, Timm Jansen, and Thomas Seidl.
Demo at Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2011


Leveraging Bagging for Evolving Data Streams

Albert Bifet, Geoff Holmes, and Bernhard Pfahringer.
In Machine Learning and Knowledge Discovery in Databases, European Conference ECML PKDD 2010 .


Sentiment knowledge discovery in Twitter streaming data

Albert Bifet and Eibe Frank.
In Proc 13th International Conference on Discovery Science, Canberra, Australia, 2010


Fast Perceptron Decision Tree Learning from Evolving Data Streams

Albert Bifet, Geoff Holmes, Bernhard Pfahringer, and Eibe Frank.
In Advances in Knowledge Discovery and Data Mining, 14th Pacific-Asia Conference, PAKDD 2010.


MOA: Massive Online Analysis

Albert Bifet, Geoff Holmes, Richard Kirkby and Bernhard Pfahringer,
In Journal of Machine Learning Research 11, 1601-1604. , 2010.


Accurate ensembles for data streams: Combining restricted Hoeffding trees using stacking

Albert Bifet, Eibe Frank, Geoff Holmes, and Bernhard Pfahringer.
In Proc 2nd Asian Conference on Machine Learning, Tokyo. JMLR, 2010 .


Improving Adaptive Bagging Methods for Evolving Data Streams

Albert Bifet, Geoff Holmes, Bernhard Pfahringer, and Ricard Gavaldà
In First Asian Conference on Machine Learning, ACML 2009, Nanjing, China, November 2009. .


New ensemble methods for evolving data streams

Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Richard Kirkby, and Ricard Gavaldà.
In 15th ACM SIGKDD Intl. Conference on Knowledge Discovery and Data Mining (KDD’09), 2009.


Handling Numeric Attributes in Hoeffding Trees

Bernhard Pfahringer, Geoffrey Holmes, and Richard Kirkby.
In Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2008.


New Options for Hoeffding Trees

Bernhard Pfahringer, Geoffrey Holmes, and Richard Kirkby.
In Australian Conference on Artificial Intelligence, pages
90-99, 2007.


Tie Breaking in Hoeffding trees

Geoffrey Holmes, Richard Kirkby, and Bernhard Pfahringer.
In J. Gama and J. S. Aguilar-Ruiz, editors, Proc Workshop W6:
Second International Workshop on Knowledge Discovery in Data Streams
, pages
107-116, 2005.


Cache hierarchy inspired compression: a novel architecture for data streams

Geoffrey Holmes, Bernhard Pfahringer, and Richard Kirkby.
In Narayanan Kulathuramaiyer, Alvin W. Yeo, Wang Yin Chai, and
Tan Chong Eng, editors, Proc Fourth International Conference on
Information Technology in Asia (CITA’05)
, pages 130-136, 2005.
12-15 December 2005.


Stress-Testing Hoeffding Trees

Geoffrey Holmes, Richard Kirkby, and Bernhard Pfahringer.
In Proc 9th European Conference on Principles and Practice of
Knowledge Discovery in Databases
, Porto, Portugal, pages 495-502. Springer,
2005.