H
Heitor Murilo Gomes
Researcher at University of Waikato
Publications - 59
Citations - 1711
Heitor Murilo Gomes is an academic researcher from University of Waikato. The author has contributed to research in topics: Data stream mining & Concept drift. The author has an hindex of 14, co-authored 58 publications receiving 993 citations. Previous affiliations of Heitor Murilo Gomes include Télécom ParisTech & Pontifícia Universidade Católica do Paraná.
Papers
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Journal ArticleDOI
Adaptive random forests for evolving data stream classification
Heitor Murilo Gomes,Albert Bifet,Jesse Read,Jean Paul Barddal,Fabrício Enembreck,Bernhard Pfharinger,Geoff Holmes,Talel Abdessalem +7 more
TL;DR: This work presents the adaptive random forest (ARF) algorithm, which includes an effective resampling method and adaptive operators that can cope with different types of concept drifts without complex optimizations for different data sets.
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A Survey on Ensemble Learning for Data Stream Classification
TL;DR: This work proposes a taxonomy for data stream ensemble learning as derived from reviewing over 60 algorithms, and important aspects such as combination, diversity, and dynamic updates, are thoroughly discussed.
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Machine learning for streaming data: state of the art, challenges, and opportunities
TL;DR: Incremental learning, online learning, and data stream learning are terms commonly associated with learning algorithms that update their models given a continuous influx of data without performing any act of reinforcement learning.
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A survey on feature drift adaptation
TL;DR: The results from the experiments indicate the need for future research in this area as even naive approaches produced gains in accuracy while reducing resources usage, and state current research topics, challenges and future directions for feature drift adaptation.
Posted Content
River: machine learning for streaming data in Python.
Jacob Montiel,Max Halford,Saulo Martiello Mastelini,Geoffrey Bolmier,Raphael Sourty,Robin Vaysse,Adil Zouitine,Heitor Murilo Gomes,Jesse Read,Talel Abdessalem,Albert Bifet +10 more
TL;DR: River is a machine learning library for dynamic data streams and continual learning that is the result from the merger of the two most popular packages for stream learning in Python: Creme and scikit-multiflow.