Publications

Google Scholar | dblp

2017

Heitor Murilo Gomes, Jean Paul Barddal, Fabrício Enembreck, and Albert Bifet: A Survey on Ensemble Learning for Data Stream Classification. ACM Comput. Surv. 50, 2, Article 23 (March 2017), 36 pages.

Diego Marron, Jesse Read, Albert Bifet, Nacho Navarro: Data stream classification using random feature functions and novel method combinations. Journal of Systems and Software 127: 195-204 (2017)

2016

João Duarte, João Gama, Albert Bifet: Adaptive Model Rules From High-Speed Data Streams. TKDD 10(3): 30 (2016)

Valentín Carela-Español, Pere Barlet-Ros, Albert Bifet, Kensuke Fukuda: A streaming flow-based technique for traffic classification applied to 12 + 1 years of Internet traffic. Telecommunication Systems 63(2): 191-204 (2016)

Gianmarco De Francisci Morales, Albert Bifet, Latifur Khan, João Gama, Wei Fan: IoT Big Data Stream Mining. KDD 2016: 2119-2120

Nicolas Kourtellis, Gianmarco De Francisci Morales, Albert Bifet, Arinto Murdopo:
VHT: Vertical Hoeffding Tree. IEEE BigData (2016)

Jean Paul Barddal, Heitor Murilo Gomes, Fabrício Enembreck, Bernhard Pfahringer, Albert Bifet: On Dynamic Feature Weighting for Feature Drifting Data Streams. ECML/PKDD (2) 2016: 129-144

Diego Marron, Jesse Read, Albert Bifet, Talel Abdessalem, Eduard Ayguadé, José R. Herrero. Echo State Hoeffding Tree Learning. ACML 2016: The 8th Asian Conference on Machine Learning 2016.

Michael Mayo, Albert Bifet: Deferral classification of evolving temporal dependent data streams. SAC 2016: 952-954

2015

Albert Bifet, Gianmarco De Francisci Morales, Jesse Read, Bernhard Pfahringer and Geoff Holmes: Efficient Online Evaluation of Big Data Stream Classifiers, KDD 2015.

Gianmarco De Francisci Morales and Albert Bifet: SAMOA: Scalable Advanced Massive Online Analysis, Journal of Machine Learning Research, 16(Jan):149–153, 2015.

Sripirakas Sakthithasan, Russel Pears, Albert Bifet, Bernhard Pfahringer:
Use of ensembles of Fourier spectra in capturing recurrent concepts in data streams. IJCNN 2015: 1-8

Albert Bifet: Mining Big Data Streams with Apache SAMOA. MUSE@PKDD/ECML 2015: 55

David Tse Jung Huang, Yun Sing Koh, Gillian Dobbie, Albert Bifet:
Drift Detection Using Stream Volatility. ECML/PKDD (1) 2015: 417-432

Albert Bifet: Real-Time Big Data Stream Analytics. SIMBig 2015: 13-14

Jesse Read, Albert Bifet: Data Stream Classification Using Random Feature Functions and Novel Method Combinations. TrustCom/BigDataSE/ISPA (2) 2015: 211-216

Jesse Read, Fernando Perez-Cruz and Albert Bifet. Deep Learning in Partially-labeled Data Streams. SAC 2015.

Indrė Žliobaitė, Albert Bifet, Jesse Read, Bernhard Pfahringer and Geoff Holmes:
Evaluation methods and decision theory for classification of streaming data with temporal dependence.
Machine Learning 98(3): 455-482 (2015)

Massimo Quadrana, Albert Bifet, Ricard Gavaldà: An efficient closed frequent itemset miner for the MOA stream mining system. AI Commun. 28(1): 143-158 (2015)

2014

Jesse Read, Antti Puurula, Albert Bifet: Multi-label Classification with Meta Labels. International Conference on Data Mining (ICDM 2014). December 2014.

Albert Bifet and Gianmarco De Francisci Morales: Big Data Stream Learning with SAMOA. Data Mining Workshop (ICDMW), 2014 IEEE International Conference on, 1199-1202. December 2014.

Brandon S Parker, Latifur Khan, Albert Bifet: Incremental Ensemble Classifier Addressing Non-stationary Fast Data Streams. Data Mining Workshop (ICDMW), 2014 IEEE International Conference on, 716-723. December 2014.

Antti Puurula, Jesse Read and Albert Bifet: Kaggle LSHTC4 Winning Solution. arXiv:1405.0546

Anh Thu Vu, Gianmarco De Francisci Morales, Joao Gama, and Albert Bifet: Distributed Adaptive Model Rules for Mining Big Data Streams. IEEE International Conference on Big Data 2014.

Diego Marron, Albert Bifet, Gianmarco De Francisci Morales: Random Forests of Very Fast Decision Trees on GPU for Mining Evolving Big Data Streams. ECAI 2014: 615-620

Dino Ienco, Albert Bifet, Bernhard Pfahringer, Pascal Poncelet: Change detection in categorical evolving data streams. SAC 2014: 792-797

Indrė Žliobaitė, Albert Bifet, Bernhard Pfahringer and Geoff Holmes: Active Learning with Drifting Streaming Data. IEEE Transactions on Neural Networks and Learning Systems 25(1): 27-39 (2014)

Joao Gama, Indrė Žliobaitė, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia: A Survey on Concept Drift Adaptation. ACM Computing Surveys, 46(4), 2014.

Dino Ienco, Albert Bifet, Bernhard Pfahringer, Pascal Poncelet: Détection de changements dans des flots de données qualitatives. EGC 2014: 517-520

2013

Albert Bifet: Mining Big Data in Real Time, Informatica Journal, Volume 37, Number 1, 2013

Konstantin Kutzkov, Albert Bifet, Francesco Bonchi, Aris Gionis STRIP: Stream Learning of Influence Probabilities KDD 2013.

Albert Bifet, Indrė Žliobaitė, Jesse Read, Bernhard Pfahringer and Geoff Holmes: Pitfalls in benchmarking data stream classification and how to avoid them ECML-PKDD 2013

Albert Bifet, Jesse Read, Bernhard Pfahringer, Geoff Holmes, and Indrė Žliobaitė: CD-MOA: Change Detection Framework for Massive Online Analysis. Proc. of the 20th Int. Symposium on Intelligent Data Analysis, IDA’13.

Dino Ienco, Albert Bifet, Indrė Žliobaitė and Bernhard Pfahringer: Clustering Based Active Learning for Evolving Data Stream DS 2013

Albert Bifet, Bernhard Pfahringer, Jesse Read, Geoff Holmes: Efficient data stream classification via probabilistic adaptive windows. SAC 2013: 801-806

Massimo Quadrana, Albert Bifet and Ricard Gavaldà: An Efficient Closed Frequent Itemset Miner for the MOA Stream Mining System IOS Press Frontiers in Artificial Intelligence and Applications, vol. 256, 157-166. Slides by Ricard Gavaldà.

2012

Wei Fan, Albert Bifet: Mining Big Data: Current Status, and Forecast to the Future. SIGKDD Explorations 14(2): 1-5 (2012) [pdf]

Jesse Read, Albert Bifet, Geoff Holmes, Bernhard Pfahringer: Scalable and efficient multi-label classification for evolving data streams. Machine Learning 88(1-2): 243-272 (2012).

Indrė Žliobaitė, Albert Bifet, Mohamed Medhat Gaber, Bogdan Gabrys, Joao Gama, Leandro L. Minku, Katarzyna Musial: Next challenges for adaptive learning systems. SIGKDD Explorations 14(1): 48-55 (2012) [pdf]

Albert Bifet, Eibe Frank, Geoff Holmes, Bernhard Pfahringer: Ensembles of Restricted Hoeffding Trees. ACM TIST 3(2): 30 (2012) [pdf]

Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl, Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Jesse Read: Stream Data Mining Using the MOA Framework. DASFAA (2) 2012: 309-313

Jesse Read, Albert Bifet, Bernhard Pfahringer, Geoff Holmes: Batch-Incremental versus Instance-Incremental Learning in Dynamic and Evolving Data. IDA 2012: 313-323

Anti Puurula and Albert Bifet. Ensembles of Sparse Multinomial Classifiers for Scalable Text Classification. In ECML/PKDD PASCAL Workshop on Large-Scale Hierarchical Classification, 2012.

2011

Albert Bifet, Geoff Holmes, Bernhard Pfahringer and Ricard Gavaldà: Mining Frequent Closed Graphs on Evolving Data Streams. 17th ACM SIGKDD Intl. Conference on Knowledge Discovery and Data Mining KDD’11.

Hardy Kremer, Philipp Kranen, Timm Jansen, Thomas Seidl, Albert Bifet, Geoff Holmes and Bernhard Pfahringer: An Effective Evaluation Measure for Clustering on Evolving Data Stream. 17th ACM SIGKDD Intl. Conference on Knowledge Discovery and Data Mining KDD’11.

Indrė Žliobaitė, Albert Bifet, Bernhard Pfahringer and Geoff Holmes: Active learning with evolving streaming data. Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2011.

Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Philipp Kranen, Hardy Kremer, Timm Jansen, and Thomas Seidl. MOA: a Real-time Analytics Open Source Framework. Demo at Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2011

Albert Bifet, Ricard Gavaldà: Mining frequent closed trees in evolving data streams. Intell. Data Anal. 15(1): 29-48 (2011)

Albert Bifet, Geoffrey Holmes, Bernhard Pfahringer, Ricard Gavaldà: Detecting Sentiment Change in Twitter Streaming Data. Journal of Machine Learning Research – Proceedings Track 17: 5-11 (2011) [pdf]

José M. Carmona-Cejudo, Manuel Baena-García, Rafael Morales Bueno, João Gama, Albert Bifet: Using GNUsmail to Compare Data Stream Mining Methods for On-line Email Classification. Journal of Machine Learning Research – Proceedings Track 17: 12-18 (2011) [pdf]

Jesse Read, Albert Bifet, Geoff Holmes, Bernhard Pfahringer: Streaming Multi-label Classification. Journal of Machine Learning Research – Proceedings Track 17: 19-25 (2011) [pdf]

Indre Zliobaite, Albert Bifet, Geoff Holmes, Bernhard Pfahringer: MOA Concept Drift Active Learning Strategies for Streaming Data. Journal of Machine Learning Research – Proceedings Track 17: 48-55 (2011) [pdf]

Albert Bifet, Geoffrey Holmes, Bernhard Pfahringer: MOA-TweetReader: Real-Time Analysis in Twitter Streaming Data. Discovery Science 2011: 46-60

José M. Carmona-Cejudo, Manuel Baena-García, José del Campo-Ávila, Albert Bifet, João Gama, Rafael Morales Bueno: Online Evaluation of Email Streaming Classifiers Using GNUsmail. IDA 2011: 90-100

2010

Albert Bifet, Geoff Holmes, and Bernhard Pfahringer. Leveraging Bagging for Evolving Data Streams.[pdf] | ECML PKDD 2010, Barcelona, Catalonia, 2010.

Albert Bifet, Eibe Frank, Geoff Holmes, and Bernhard Pfahringer. “Accurate ensembles for data streams: Combining restricted Hoeffding trees using stacking.” [pdf] Proc 2nd Asian Conference on Machine Learning, Tokyo. JMLR, 2010.

Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl, Albert Bifet, Geoff Holmes, Bernhard Pfahringer: Clustering Performance on Evolving Data Streams: Assessing Algorithms and Evaluation Measures within MOA. ICDM Workshops 2010: 1400-1403

José M. Carmona-Cejudo, Manuel Baena-García, José del Campo-Ávila, Rafael Morales Bueno, Albert Bifet: GNUsmail: Open Framework for On-line Email Classification. ECAI 2010: 1141-1142

Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl. “MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering”. [pdf]Workshop on Applications of Pattern Analysis, JMLR, 2010.

Albert Bifet and Eibe Frank. “Sentiment knowledge discovery in Twitter streaming data”. [pdf] Proc 13th International Conference on Discovery Science, Canberra, Australia.

Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl. “MOA: Massive online analysis, a framework for stream classification and clustering.” [pdf]Pechenizkiy, M. and Žliobaitė, I. (eds), HaCDAIS Workshop ECML-PKDD 2010, 2010.

Albert Bifet, Geoff Holmes, Bernhard Pfahringer, and Eibe Frank. “Fast Perceptron Decision Tree Learning from Evolving Data Streams” [pdf] Advances in Knowledge Discovery and Data Mining, 14th Pacific-Asia Conference, PAKDD 2010.

Albert Bifet, Geoff Holmes, Richard Kirkby, and Bernhard Pfahringer. “MOA: Massive online analysis.”Journal of Machine Learning Research (JMLR), 2010.

José Luis Balcázar, Albert Bifet and Antoni Lozano: ” Mining Frequent Closed Rooted Trees “. [pdf] | Machine Learning Journal (78:1), 2010, 1-33.

2009

Albert Bifet Thesis: Adaptive Learning and Mining for Data Streams and Frequent Patterns Advised byRicard Gavaldà and José Luis Balcázar.

Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Richard Kirkby, and Ricard Gavaldà: ” New ensemble methods for evolving data streams” . In 15th ACM SIGKDD Intl. Conference on Knowledge Discovery and Data Mining (KDD’09), Paris, France, June 2009.

Albert Bifet, Geoff Holmes, Bernhard Pfahringer, and Ricard Gavaldà: “Improving Adaptive Bagging Methods for Evolving Data Streams” . In First Asian Conference on Machine Learning, ACML 2009, Nanjing, China, November 2009.

Albert Bifet and Ricard Gavaldà: “ Adaptive XML Tree Classification on Evolving Data Streams“. [pdf] | ECML PKDD 2009

Albert Bifet and Ricard Gavaldà: “ Adaptive Learning from Evolving Data Streams“. | IDA 2009 | Extended Version Technical Report LSI R09-9

Albert Bifet: Adaptive learning and mining for data streams and frequent patterns. SIGKDD Explorations 11(1): 55-56 (2009)

2008

Albert Bifet and Ricard Gavaldà: “ Mining adaptively frequent closed unlabeled rooted trees in data streams“. [pdf] | [Software] | [Datasets] | [Slides] . In 14th ACM SIGKDD Intl. Conference on Knowledge Discovery and Data Mining (KDD’08), Las Vegas, USA, August 2008.

José Luis Balcázar, Albert Bifet and Antoni Lozano: “ Mining Implications from Lattices of Closed Trees “.[pdf][slides] | Extraction et Gestion des Connaissances EGC’2008, Sophia Antipolis, France.

2007

José Luis Balcázar, Albert Bifet and Antoni Lozano: “ Closed and maximal tree mining using natural representations“. [pdf] | Workshop on Mining and Learning with Graphs MLG 2007.

José Luis Balcázar, Albert Bifet and Antoni Lozano: “ Subtree Testing and Closed Tree Mining Through Natural Representations “. [pdf] | ACKE Workshop on “Advances in Conceptual Knowledge Engineering” 2007, Regensburg, Germany.

José Luis Balcázar, Albert Bifet and Antoni Lozano: “ Mining Frequent Closed Unordered Trees Through Natural Representations “. [pdf] | [slides] | In 2007 International Conference on Conceptual Structures, Sheffield UK.

Albert Bifet and Ricard Gavaldà: “ Learning from Time-Changing Data with Adaptive Windowing“. [pdf] |[pdf extended version] . Poster. In 2007 SIAM International Conference on Data Mining (SDM’07), Minneapolis, Minnesota.

2006

Albert Bifet and Ricard Gavaldà: “ Kalman Filters and Adaptive Windows for Learning in Data Streams “.[pdf] | [pdf extended version][slides] | Proc. 9th International Conference on Discovery Sicence (DS 2006). Springer-Verlag Lecture Notes in Artificial Intelligence 4265, 29-40.

José Luis Balcázar, Albert Bifet and Antoni Lozano: “ Intersection Algorithms and a Closure Operator on Unordered Trees “. [pdf] | Workshop Mining and Learning with Graphs MLG 2006.

Manuel Baena-García, José del Campo-Ávila, Raúl Fidalgo, Albert Bifet , Ricard Gavaldà and Rafael Morales-Bueno: “ Early Drift Detection Method “. [pdf] | ECML PKDD Workshop on Knowledge Discovery from Data Streams 2006.

2005

Albert Bifet, Carlos Castillo, Paul-Alexandru Chirita and Ingmar Weber: “ An Analysis of Factors Used in Search Engine Ranking“.| [pdf] | [slides] Workshop on Adversarial Information Retrieval on the Web (synopsis), 2005.