I am Professor at LTCI, Telecom ParisTech, Head of the Data, Intelligence and Graphs (DIG) Group at Telecom ParisTech, and Scientific Collaborator at Ecole Polytechnique. My research focuses on Machine Learning for Data Streams, Big Data Machine Learning and Artificial Intelligence. Problems I investigate are motivated by large scale data, the Internet of Things (IoT), and Big Data Science.
I am also co-leading the open source projects MOA Massive On-line Analysis and Apache SAMOA Scalable Advanced Massive Online Analysis.
Machine Learning for Data Streams: with Practical Examples in MOA
- Series: Adaptive Computation and Machine Learning series
- Hardcover: 288 pages
- Publisher: The MIT Press (March 2, 2018)
- Language: English
- ISBN-10: 0262037793
- ISBN-13: 978-0262037792
Today many information sources—including sensor networks, financial markets, social networks, and healthcare monitoring—are so-called data streams, arriving sequentially and at high speed. Analysis must take place in real time, with partial data and without the capacity to store the entire data set. This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations.
- ACM Distinguished Speaker
- Invited Keynote Speaker KDD Workshop on Mining and Learning from Time Series (MiLeTS 2017)
- Invited Speaker The 8th Asian Conference on Machine Learning (ACML 2016)
- Invited Speaker The 16th International Conference on Artificial Intelligence and Soft Computing ICAISC 2017
- Tutorial on IoT in Practice: Case Studies in Data Analytics, with Issues in Privacy and Security. Held at KDD 2017 (August 13, Halifax, Canada)
- Hands-on Tutorial on Massive Online Analytics. Held at KDD 2017 (August 15, Halifax, Canada)
- KDD BigMine 2017. A forum bringing together researchers exploring all aspects of Big Data and IoT Analytics. Held at KDD 2017 (August 14, Halifax, Canada)
- Albert Bifet, Jiajin Zhang, Wei Fan, Cheng He, Jianfeng Zhang, Jianfeng Qian, Geoff Holmes, Bernhard Pfahringer: Extremely Fast Decision Tree Mining for Evolving Data Streams. KDD 2017: 1733-1742
- Albert Bifet: Classifier Concept Drift Detection and the Illusion of Progress. ICAISC (2) 2017: 715-725
- Heitor Murilo Gomes, Albert Bifet, Jesse Read, Jean Paul Barddal, Fabrício Enembreck, Bernhard Pfharinger, Geoff Holmes, Talel Abdessalem: Adaptive random forests for evolving data stream classiﬁcation. Machine Learning, Springer, 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)