Albert Bifet

Affiliations:
  • University of Waikato, Hamilton, New Zealand


According to our database1, Albert Bifet authored at least 233 papers between 2005 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Bibliography

2023
Editorial: Seventh special issue on Knowledge Discovery and Business Intelligence.
Expert Syst. J. Knowl. Eng., December, 2023

Exploring the potentials of online machine learning for predictive maintenance: a case study in the railway industry.
Appl. Intell., December, 2023

STUDD: a student-teacher method for unsupervised concept drift detection.
Mach. Learn., November, 2023

teex: A toolbox for the evaluation of explanations.
Neurocomputing, October, 2023

Balancing Performance and Energy Consumption of Bagging Ensembles for the Classification of Data Streams in Edge Computing.
IEEE Trans. Netw. Serv. Manag., September, 2023

Combining Diverse Meta-Features to Accurately Identify Recurring Concept Drift in Data Streams.
ACM Trans. Knowl. Discov. Data, 2023

Wangiri Fraud: Pattern Analysis and Machine-Learning-Based Detection.
IEEE Internet Things J., 2023

A Survey on Semi-supervised Learning for Delayed Partially Labelled Data Streams.
ACM Comput. Surv., 2023

BELLA: Black box model Explanations by Local Linear Approximations.
CoRR, 2023

Preventing Discriminatory Decision-making in Evolving Data Streams.
CoRR, 2023

Aging and rejuvenating strategies for fading windows in multi-label classification on data streams.
Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, 2023

ORSUM 2023 - 6th Workshop on Online Recommender Systems and User Modeling.
Proceedings of the 17th ACM Conference on Recommender Systems, 2023

FG<sup>2</sup>AN: Fairness-Aware Graph Generative Adversarial Networks.
Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

Survey on Online Streaming Continual Learning.
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

StreamAI: Dealing with Challenges of Continual Learning Systems for Serving AI in Production.
Proceedings of the 45th IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice, 2023

FALL: A Modular Adaptive Learning Platform for Streaming Data.
Proceedings of the 39th IEEE International Conference on Data Engineering, 2023

StreamMLOps: Operationalizing Online Learning for Big Data Streaming & Real-Time Applications.
Proceedings of the 39th IEEE International Conference on Data Engineering, 2023

Preventing Discriminatory Decision-making in Evolving Data Streams.
Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023

Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class Incremental Learning.
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023

Choosing the Right Time to Learn Evolving Data Streams.
Proceedings of the IEEE International Conference on Big Data, 2023

2022
A Survey on Spatio-temporal Data Analytics Systems.
ACM Comput. Surv., January, 2022

Preface to the special issue on dynamic recommender systems and user models.
User Model. User Adapt. Interact., 2022

Open challenges for Machine Learning based Early Decision-Making research.
SIGKDD Explor., 2022

Analyzing and repairing concept drift adaptation in data stream classification.
Mach. Learn., 2022

TA4L: Efficient temporal abstraction of multivariate time series.
Knowl. Based Syst., 2022

LP-ROBIN: Link prediction in dynamic networks exploiting incremental node embedding.
Inf. Sci., 2022

Resource-Aware Edge-Based Stream Analytics.
IEEE Internet Comput., 2022

VEPRECO: Vertical databases with pre-pruning strategies and common candidate selection policies to fasten sequential pattern mining.
Expert Syst. Appl., 2022

SOKNL: A novel way of integrating K-nearest neighbours with adaptive random forest regression for data streams.
Data Min. Knowl. Discov., 2022

An eager splitting strategy for online decision trees in ensembles.
Data Min. Knowl. Discov., 2022

Linear TreeShap.
CoRR, 2022

Green Accelerated Hoeffding Tree.
CoRR, 2022

Incremental Mining of Frequent Serial Episodes Considering Multiple Occurrence.
CoRR, 2022

Proceedings of the 4th Workshop on Online Recommender Systems and User Modeling - ORSUM 2021.
CoRR, 2022

Assessing Vulnerability from Its Description.
Proceedings of the Ubiquitous Security - Second International Conference, 2022

ORSUM 2022 - 5th Workshop on Online Recommender Systems and User Modeling.
Proceedings of the RecSys '22: Sixteenth ACM Conference on Recommender Systems, Seattle, WA, USA, September 18, 2022

Evolution-Based Online Automated Machine Learning.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2022

A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Linear tree shap.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Online Clustering: Algorithms, Evaluation, Metrics, Applications and Benchmarking.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Online Hyperparameter Optimization for Streaming Neural Networks.
Proceedings of the International Joint Conference on Neural Networks, 2022

Adaptive Model Compression of Ensembles for Evolving Data Streams Forecasting.
Proceedings of the International Joint Conference on Neural Networks, 2022

Incremental Mining of Frequent Serial Episodes Considering Multiple Occurrences.
Proceedings of the Computational Science - ICCS 2022, 2022

Adaptive Online Domain Incremental Continual Learning.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2022, 2022

A Probabilistic Framework for Adapting to Changing and Recurring Concepts in Data Streams.
Proceedings of the 9th IEEE International Conference on Data Science and Advanced Analytics, 2022

Adaptive Neural Networks for Online Domain Incremental Continual Learning.
Proceedings of the Discovery Science - 25th International Conference, 2022

Continuous Health Monitoring of Machinery using Online Clustering on Unlabeled Data Streams.
Proceedings of the IEEE International Conference on Big Data, 2022

StreamFlow: A System for Summarizing and Learning Over Industrial Big Data Streams.
Proceedings of the IEEE International Conference on Big Data, 2022

Stream2Graph: Dynamic Knowledge Graph for Online Learning Applied in Large-scale Network.
Proceedings of the IEEE International Conference on Big Data, 2022

2021
Data stream analysis: Foundations, major tasks and tools.
WIREs Data Mining Knowl. Discov., 2021

Learning from evolving data streams through ensembles of random patches.
Knowl. Inf. Syst., 2021

River: machine learning for streaming data in Python.
J. Mach. Learn. Res., 2021

Improving the performance of bagging ensembles for data streams through mini-batching.
Inf. Sci., 2021

Energy modeling of Hoeffding tree ensembles.
Intell. Data Anal., 2021

Exact and Approximate Algorithms for Computing Betweenness Centrality in Directed Graphs.
Fundam. Informaticae, 2021

vertTIRP: Robust and efficient vertical frequent time interval-related pattern mining.
Expert Syst. Appl., 2021

Binding data mining and expert knowledge for one-day-ahead prediction of hourly global solar radiation.
Expert Syst. Appl., 2021

CURIE: a cellular automaton for concept drift detection.
Data Min. Knowl. Discov., 2021

Recurring concept memory management in data streams: exploiting data stream concept evolution to improve performance and transparency.
Data Min. Knowl. Discov., 2021

Sketches for Time-Dependent Machine Learning.
CoRR, 2021

Model Compression for Dynamic Forecast Combination.
CoRR, 2021

AI Transformation in the Public Sector: Ongoing Research.
Proceedings of the Swedish Artificial Intelligence Society Workshop, 2021

ORSUM 2021 - 4th Workshop on Online Recommender Systems and User Modeling.
Proceedings of the RecSys '21: Fifteenth ACM Conference on Recommender Systems, Amsterdam, The Netherlands, 27 September 2021, 2021

Studying and Exploiting the Relationship Between Model Accuracy and Explanation Quality.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

FARF: A Fair and Adaptive Random Forests Classifier.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2021

Fast and lightweight binary and multi-branch Hoeffding Tree Regressors.
Proceedings of the 2021 International Conference on Data Mining, 2021

Confident Interpretations of Black Box Classifiers.
Proceedings of the International Joint Conference on Neural Networks, 2021

Fingerprinting Concepts in Data Streams with Supervised and Unsupervised Meta-Information.
Proceedings of the 37th IEEE International Conference on Data Engineering, 2021

Incremental k-Nearest Neighbors Using Reservoir Sampling for Data Streams.
Proceedings of the Discovery Science - 24th International Conference, 2021

S2CE: a hybrid cloud and edge orchestrator for mining exascale distributed streams.
Proceedings of the 15th ACM International Conference on Distributed and Event-based Systems, 2021

Kalman Filtering for Learning with Evolving Data Streams.
Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

Challenges of Machine Learning for Data Streams in the Banking Industry.
Proceedings of the Big Data Analytics - 9th International Conference, 2021

2020
Spiking Neural Networks and online learning: An overview and perspectives.
Neural Networks, 2020

Exploiting the stimuli encoding scheme of evolving Spiking Neural Networks for stream learning.
Neural Networks, 2020

Discriminative Streaming Network Embedding.
Knowl. Based Syst., 2020

SCALAR - A Platform for Real-time Machine Learning Competitions on Data Streams.
J. Open Source Softw., 2020

Sampling informative patterns from large single networks.
Future Gener. Comput. Syst., 2020

Fifth special issue on knowledge discovery and business intelligence.
Expert Syst. J. Knowl. Eng., 2020

Delayed labelling evaluation for data streams.
Data Min. Knowl. Discov., 2020

Machine Learning (In) Security: A Stream of Problems.
CoRR, 2020

An Eager Splitting Strategy for Online Decision Trees.
CoRR, 2020

Emergent and Unspecified Behaviors in Streaming Decision Trees.
CoRR, 2020

IoT data stream analytics.
Ann. des Télécommunications, 2020

ORSUM - Workshop on Online Recommender Systems and User Modeling.
Proceedings of the RecSys 2020: Fourteenth ACM Conference on Recommender Systems, 2020

Challenges of Stream Learning for Predictive Maintenance in the Railway Sector.
Proceedings of the IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning, 2020

confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms.
Proceedings of the Learning and Intelligent Optimization - 14th International Conference, 2020

Adaptive XGBoost for Evolving Data Streams.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Randomizing the Self-Adjusting Memory for Enhanced Handling of Concept Drift.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Performance measures for evolving predictions under delayed labelling classification.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

On Ensemble Techniques for Data Stream Regression.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

CS-ARF: Compressed Adaptive Random Forests for Evolving Data Stream Classification.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Survey on Feature Transformation Techniques for Data Streams.
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Efficient Batch-Incremental Classification Using UMAP for Evolving Data Streams.
Proceedings of the Advances in Intelligent Data Analysis XVIII, 2020

Fast Incremental Naïve Bayes with Kalman Filtering.
Proceedings of the 20th International Conference on Data Mining Workshops, 2020

Incremental Rebalancing Learning on Evolving Data Streams.
Proceedings of the 20th International Conference on Data Mining Workshops, 2020

Improving parallel performance of ensemble learners for streaming data through data locality with mini-batching.
Proceedings of the 22nd IEEE International Conference on High Performance Computing and Communications; 18th IEEE International Conference on Smart City; 6th IEEE International Conference on Data Science and Systems, 2020

Compressed k-Nearest Neighbors Ensembles for Evolving Data Streams.
Proceedings of the ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020, 2020

FEAT: A Fairness-Enhancing and Concept-Adapting Decision Tree Classifier.
Proceedings of the Discovery Science - 23rd International Conference, 2020

Unsupervised Concept Drift Detection Using a Student-Teacher Approach.
Proceedings of the Discovery Science - 23rd International Conference, 2020

Streaming Time Series Forecasting using Multi-Target Regression with Dynamic Ensemble Selection.
Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

C-SMOTE: Continuous Synthetic Minority Oversampling for Evolving Data Streams.
Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

AutoML for Stream k-Nearest Neighbors Classification.
Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

2019
Machine learning for streaming data: state of the art, challenges, and opportunities.
SIGKDD Explor., 2019

Correction to: Adaptive random forests for evolving data stream classification.
Mach. Learn., 2019

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

Introduction to the special issue on Big Data, IoT Streams and Heterogeneous Source Mining.
Int. J. Data Sci. Anal., 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

Measuring the Shattering coefficient of Decision Tree models.
Expert Syst. Appl., 2019

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

Recurring concept meta-learning for evolving data streams.
Expert Syst. Appl., 2019

Rebalancing Learning on Evolving Data Streams.
CoRR, 2019

Exploiting a Stimuli Encoding Scheme of Spiking Neural Networks for Stream Learning.
CoRR, 2019

Resource-aware Elastic Swap Random Forest for Evolving Data Streams.
CoRR, 2019

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

ORSUM 2019 2nd workshop on online recommender systems and user modeling.
Proceedings of the 13th ACM Conference on Recommender Systems, 2019

Towards Automated Configuration of Stream Clustering Algorithms.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

IDSA-IoT: An Intrusion Detection System Architecture for IoT Networks.
Proceedings of the 2019 IEEE Symposium on Computers and Communications, 2019

Adaptive Random Forests with Resampling for Imbalanced data Streams.
Proceedings of the International Joint Conference on Neural Networks, 2019

Network of Experts: Learning from Evolving Data Streams Through Network-Based Ensembles.
Proceedings of the Neural Information Processing - 26th International Conference, 2019

Streaming Random Patches for Evolving Data Stream Classification.
Proceedings of the 2019 IEEE International Conference on Data Mining, 2019

Recent trends in streaming data analysis, concept drift and analysis of dynamic data sets.
Proceedings of the 27th European Symposium on Artificial Neural Networks, 2019

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

Adaptive Algorithms for Estimating Betweenness and <i>k</i>-path Centralities.
Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019

Semi-supervised Learning over Streaming Data using MOA.
Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

Feature Scoring using Tree-Based Ensembles for Evolving Data Streams.
Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

Real-Time Machine Learning Competition on Data Streams at the IEEE Big Data 2019.
Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

Arbitrated Dynamic Ensemble with Abstaining for Time-Series Forecasting on Data Streams.
Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 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

Novel Adaptive Algorithms for Estimating Betweenness, Coverage and k-path Centralities.
CoRR, 2018

Large-Scale Learning from Data Streams with Apache SAMOA.
CoRR, 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 (IEEE BigData 2018), 2018

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

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

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

2017
Adaptive random forests for evolving data stream classification.
Mach. Learn., 2017

Data stream classification using random feature functions and novel method combinations.
J. Syst. Softw., 2017

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

Discriminative Distance-Based Network Indices and the Tiny-World Property.
CoRR, 2017

Metropolis-Hastings Algorithms for Estimating Betweenness Centrality in Large Networks.
CoRR, 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 (IEEE BigData 2017), 2017

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

2016
Adaptive Model Rules From High-Speed Data Streams.
ACM Trans. Knowl. Discov. Data, 2016

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

Analyzing Big Data Streams with Apache SAMOA.
Proceedings of the Behavioral Analytics in Social and Ubiquitous Environments, 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 (IEEE BigData 2016), 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.
Mach. Learn., 2015

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

An efficient closed frequent itemset miner for the MOA stream mining system.
AI Commun., 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

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 Networks Learn. Syst., 2014

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

Kaggle LSHTC4 Winning Solution.
CoRR, 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 (IEEE BigData 2014), 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

2012
Ensembles of Restricted Hoeffding Trees.
ACM Trans. Intell. Syst. Technol., 2012

Next challenges for adaptive learning systems.
SIGKDD Explor., 2012

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

Scalable and efficient multi-label classification for evolving data streams.
Mach. Learn., 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.
Mach. Learn., 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.
PhD thesis, 2009

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