M
Mohamed-Rafik Bouguelia
Researcher at Halmstad University
Publications - 38
Citations - 438
Mohamed-Rafik Bouguelia is an academic researcher from Halmstad University. The author has contributed to research in topics: Computer science & Cluster analysis. The author has an hindex of 11, co-authored 33 publications receiving 320 citations. Previous affiliations of Mohamed-Rafik Bouguelia include University of Lorraine.
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Journal ArticleDOI
Agreeing to disagree: active learning with noisy labels without crowdsourcing
TL;DR: This work proposes a new active learning method for classification, which handles label noise without relying on multiple oracles (i.e., crowdsourcing), and proposes a strategy that selects (for labeling) instances with a high influence on the learned model.
Journal ArticleDOI
Efficient Activity Recognition in Smart Homes Using Delayed Fuzzy Temporal Windows on Binary Sensors
Rebeen Ali Hamad,Alberto Gabriel Salguero Hidalgo,Mohamed-Rafik Bouguelia,Macarena Espinilla Estevez,Javier Medina Quero +4 more
TL;DR: A method that delays the recognition process in order to include some sensor activations that occur after the point in time where the decision needs to be made, and that the representation with fuzzy temporal windows enhances performance within deep learning models.
Proceedings ArticleDOI
A Stream-Based Semi-supervised Active Learning Approach for Document Classification
TL;DR: A new stream-based semi supervised active learning method for document classification, which is able to actively query (from a human annotator) the class-labels of documents that are most informative for learning, according to an uncertainty measure is proposed.
Journal ArticleDOI
An adaptive streaming active learning strategy based on instance weighting
TL;DR: A new query strategy based on instance weighting that improves the performance of the active learner compared to the commonly used uncertainty strategies is proposed, and an adaptive uncertainty threshold which is suitable for the streaming setting is proposed.
Journal ArticleDOI
Unsupervised classification of slip events for planetary exploration rovers
TL;DR: An unsupervised method for the classification of discrete rovers’ slip events based on proprioceptive signals is introduced, able to automatically discover and track various degrees of slip (i.e. low slip, moderate slip, high slip).