Albert Bifet

According to our database1, Albert Bifet authored at least 105 papers between 2005 and 2019.

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Bibliography

2019
Boosting decision stumps for dynamic feature selection on data streams.
Inf. Syst., 2019

Efficient frequent subgraph mining on large streaming graphs.
Intell. Data Anal., 2019

On learning guarantees to unsupervised concept drift detection on data streams.
Expert Syst. Appl., 2019

Merit-guided dynamic feature selection filter for data streams.
Expert Syst. Appl., 2019

Continuous Analytics of Web Streams.
Proceedings of the Companion of The 2019 World Wide Web Conference, 2019

Metropolis-Hastings Algorithms for Estimating Betweenness Centrality.
Proceedings of the Advances in Database Technology, 2019

2018
Scikit-Multiflow: A Multi-output Streaming Framework.
J. Mach. Learn. Res., 2018

Predicting attributes and friends of mobile users from AP-Trajectories.
Inf. Sci., 2018

Discriminative Distance-Based Network Indices with Application to Link Prediction.
Comput. J., 2018

Telemetry-based stream-learning of BGP anomalies.
Proceedings of the 2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, 2018

Scalable Model-Based Cascaded Imputation of Missing Data.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2018

Efficient Exact and Approximate Algorithms for Computing Betweenness Centrality in Directed Graphs.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2018

Unsupervised real-time detection of BGP anomalies leveraging high-rate and fine-grained telemetry data.
Proceedings of the IEEE INFOCOM 2018, 2018

EXAD: A System for Explainable Anomaly Detection on Big Data Traces.
Proceedings of the 2018 IEEE International Conference on Data Mining Workshops, 2018

Bitcoin Volatility Forecasting with a Glimpse into Buy and Sell Orders.
Proceedings of the IEEE International Conference on Data Mining, 2018

Adaptive random forests for data stream regression.
Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

Ubiquitous Artificial Intelligence and Dynamic Data Streams.
Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems, 2018

Learning Fast and Slow: A Unified Batch/Stream Framework.
Proceedings of the IEEE International Conference on Big Data, 2018

DyBED: An Efficient Algorithm for Updating Betweenness Centrality in Directed Dynamic Graphs.
Proceedings of the IEEE International Conference on Big Data, 2018

An In-depth Comparison of Group Betweenness Centrality Estimation Algorithms.
Proceedings of the IEEE International Conference on Big Data, 2018

A Sketch-Based Naive Bayes Algorithms for Evolving Data Streams.
Proceedings of the IEEE International Conference on Big Data, 2018

2017
Adaptive random forests for evolving data stream classification.
Machine Learning, 2017

Data stream classification using random feature functions and novel method combinations.
Journal of Systems and Software, 2017

A Survey on Ensemble Learning for Data Stream Classification.
ACM Comput. Surv., 2017

Inferring Demographics and Social Networks of Mobile Device Users on Campus From AP-Trajectories.
Proceedings of the 26th International Conference on World Wide Web Companion, 2017

Extremely Fast Decision Tree Mining for Evolving Data Streams.
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 2017

Droplet Ensemble Learning on Drifting Data Streams.
Proceedings of the Advances in Intelligent Data Analysis XVI, 2017

Classifier Concept Drift Detection and the Illusion of Progress.
Proceedings of the Artificial Intelligence and Soft Computing, 2017

Predicting over-indebtedness on batch and streaming data.
Proceedings of the 2017 IEEE International Conference on Big Data, BigData 2017, 2017

Low-latency multi-threaded ensemble learning for dynamic big data streams.
Proceedings of the 2017 IEEE International Conference on Big Data, BigData 2017, 2017

2016
Adaptive Model Rules From High-Speed Data Streams.
TKDD, 2016

A streaming flow-based technique for traffic classification applied to 12 + 1 years of Internet traffic.
Telecommunication Systems, 2016

Mining Internet of Things (IoT) Big Data Streams.
Proceedings of the 3rd Annual International Symposium on Information Management and Big Data, 2016

Deferral classification of evolving temporal dependent data streams.
Proceedings of the 31st Annual ACM Symposium on Applied Computing, 2016

On Dynamic Feature Weighting for Feature Drifting Data Streams.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2016

IoT Big Data Stream Mining.
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

VHT: Vertical hoeffding tree.
Proceedings of the 2016 IEEE International Conference on Big Data, 2016

Echo State Hoeffding Tree Learning.
Proceedings of The 8th Asian Conference on Machine Learning, 2016

2015
Evaluation methods and decision theory for classification of streaming data with temporal dependence.
Machine Learning, 2015

SAMOA: scalable advanced massive online analysis.
J. Mach. Learn. Res., 2015

Data Stream Classification Using Random Feature Functions and Novel Method Combinations.
Proceedings of the 2015 IEEE TrustCom/BigDataSE/ISPA, 2015

Real-Time Big Data Stream Analytics.
Proceedings of the 2nd Annual International Symposium on Information Management and Big Data, 2015

Deep learning in partially-labeled data streams.
Proceedings of the 30th Annual ACM Symposium on Applied Computing, 2015

Drift Detection Using Stream Volatility.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

Mining Big Data Streams with Apache SAMOA.
Proceedings of the 6th International Workshop on Mining Ubiquitous and Social Environments (MUSE 2015) co-located with the 26th European Conference on Machine Learning / 19th European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2015), 2015

Preface.
Proceedings of the 4th International Workshop on Big Data, 2015

Efficient Online Evaluation of Big Data Stream Classifiers.
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

Use of ensembles of Fourier spectra in capturing recurrent concepts in data streams.
Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

StreamDM: Advanced Data Mining in Spark Streaming.
Proceedings of the IEEE International Conference on Data Mining Workshop, 2015

2014
Active Learning With Drifting Streaming Data.
IEEE Trans. Neural Netw. Learning Syst., 2014

A survey on concept drift adaptation.
ACM Comput. Surv., 2014

Change detection in categorical evolving data streams.
Proceedings of the Symposium on Applied Computing, 2014

Preface.
Proceedings of the 3rd International Workshop on Big Data, 2014

Multi-label Classification with Meta-Labels.
Proceedings of the 2014 IEEE International Conference on Data Mining, 2014

Incremental Ensemble Classifier Addressing Non-stationary Fast Data Streams.
Proceedings of the 2014 IEEE International Conference on Data Mining Workshops, 2014

Big Data Stream Learning with SAMOA.
Proceedings of the 2014 IEEE International Conference on Data Mining Workshops, 2014

Détection de changements dans des flots de données qualitatives.
Proceedings of the 14èmes Journées Francophones Extraction et Gestion des Connaissances, 2014

Random Forests of Very Fast Decision Trees on GPU for Mining Evolving Big Data Streams.
Proceedings of the ECAI 2014 - 21st European Conference on Artificial Intelligence, 18-22 August 2014, Prague, Czech Republic, 2014

Distributed Adaptive Model Rules for mining big data streams.
Proceedings of the 2014 IEEE International Conference on Big Data, 2014

2013
Mining Big Data in Real Time.
Informatica (Slovenia), 2013

Efficient data stream classification via probabilistic adaptive windows.
Proceedings of the 28th Annual ACM Symposium on Applied Computing, 2013

Pitfalls in Benchmarking Data Stream Classification and How to Avoid Them.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013

STRIP: stream learning of influence probabilities.
Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

CD-MOA: Change Detection Framework for Massive Online Analysis.
Proceedings of the Advances in Intelligent Data Analysis XII, 2013

Clustering Based Active Learning for Evolving Data Streams.
Proceedings of the Discovery Science - 16th International Conference, 2013

An Efficient Closed Frequent Itemset Miner for the Moa Stream Mining System.
Proceedings of the Artificial Intelligence Research and Development, 2013

2012
Ensembles of Restricted Hoeffding Trees.
ACM TIST, 2012

Next challenges for adaptive learning systems.
SIGKDD Explorations, 2012

Mining big data: current status, and forecast to the future.
SIGKDD Explorations, 2012

Scalable and efficient multi-label classification for evolving data streams.
Machine Learning, 2012

Batch-Incremental versus Instance-Incremental Learning in Dynamic and Evolving Data.
Proceedings of the Advances in Intelligent Data Analysis XI - 11th International Symposium, 2012

Stream Data Mining Using the MOA Framework.
Proceedings of the Database Systems for Advanced Applications, 2012

2011
MOA Concept Drift Active Learning Strategies for Streaming Data.
Proceedings of the Second Workshop on Applications of Pattern Analysis, 2011

Streaming Multi-label Classification.
Proceedings of the Second Workshop on Applications of Pattern Analysis, 2011

Using GNUsmail to Compare Data Stream Mining Methods for On-line Email Classification.
Proceedings of the Second Workshop on Applications of Pattern Analysis, 2011

Detecting Sentiment Change in Twitter Streaming Data.
Proceedings of the Second Workshop on Applications of Pattern Analysis, 2011

Mining frequent closed trees in evolving data streams.
Intell. Data Anal., 2011

Active Learning with Evolving Streaming Data.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2011

MOA: A Real-Time Analytics Open Source Framework.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2011

An effective evaluation measure for clustering on evolving data streams.
Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011

Mining frequent closed graphs on evolving data streams.
Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011

Online Evaluation of Email Streaming Classifiers Using GNUsmail.
Proceedings of the Advances in Intelligent Data Analysis X - 10th International Symposium, 2011

MOA-TweetReader: Real-Time Analysis in Twitter Streaming Data.
Proceedings of the Discovery Science - 14th International Conference, 2011

2010
Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
Frontiers in Artificial Intelligence and Applications 207, IOS Press, ISBN: 978-1-60750-090-2, 2010

Mining frequent closed rooted trees.
Machine Learning, 2010

MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering.
Proceedings of the First Workshop on Applications of Pattern Analysis, 2010

MOA: Massive Online Analysis.
J. Mach. Learn. Res., 2010

Accurate Ensembles for Data Streams: Combining Restricted Hoeffding Trees using Stacking.
Proceedings of the 2nd Asian Conference on Machine Learning, 2010

Leveraging Bagging for Evolving Data Streams.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2010

Fast Perceptron Decision Tree Learning from Evolving Data Streams.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2010

Clustering Performance on Evolving Data Streams: Assessing Algorithms and Evaluation Measures within MOA.
Proceedings of the ICDMW 2010, 2010

GNUsmail: Open Framework for On-line Email Classification.
Proceedings of the ECAI 2010, 2010

Sentiment Knowledge Discovery in Twitter Streaming Data.
Proceedings of the Discovery Science - 13th International Conference, 2010

2009
Adaptive learning and mining for data streams and frequent patterns.
SIGKDD Explorations, 2009

Adaptive XML Tree Classification on Evolving Data Streams.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2009

New ensemble methods for evolving data streams.
Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28, 2009

Adaptive Learning from Evolving Data Streams.
Proceedings of the Advances in Intelligent Data Analysis VIII, 2009

Improving Adaptive Bagging Methods for Evolving Data Streams.
Proceedings of the Advances in Machine Learning, 2009

2008
Mining adaptively frequent closed unlabeled rooted trees in data streams.
Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008

Mining Implications from Lattices of Closed Trees.
Proceedings of the Extraction et gestion des connaissances (EGC'2008), 2008

2007
Learning from Time-Changing Data with Adaptive Windowing.
Proceedings of the Seventh SIAM International Conference on Data Mining, 2007

Mining Frequent Closed Unordered Trees Through Natural Representations.
Proceedings of the Conceptual Structures: Knowledge Architectures for Smart Applications, 2007

Subtree Testing and Closed Tree Mining Through Natural Representations.
Proceedings of the 18th International Workshop on Database and Expert Systems Applications (DEXA 2007), 2007

2006
Kalman Filters and Adaptive Windows for Learning in Data Streams.
Proceedings of the Discovery Science, 9th International Conference, 2006

2005
An Analysis of Factors Used in Search Engine Ranking.
Proceedings of the AIRWeb 2005, 2005


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