102 citations · 103 across the 5 of their papers we have counts for
5 papers
Classification Tree-based Active Learning: A Wrapper Approach
Ashna Jose, Emilie Devijver, Massih-Reza Amini +2
Supervised machine learning often requires large training sets to train accurate models, yet obtaining large amounts of labeled data is not always feasible. Hence, it becomes cruci…
Pool-Based Active Learning with Proper Topological Regions
Lies Hadjadj, Emilie Devijver, Remi Molinier +1
Machine learning methods usually rely on large sample size to have good performance, while it is difficult to provide labeled set in many applications. Pool-based active learning m…
Self-Training: A Survey
Massih-Reza Amini, Vasilii Feofanov, Loic Pauletto +3
Semi-supervised algorithms aim to learn prediction functions from a small set of labeled observations and a large set of unlabeled observations. Because this framework is relevant…
Multi-class Probabilistic Bounds for Self-learning
Vasilii Feofanov, Emilie Devijver, Massih-Reza Amini
Self-learning is a classical approach for learning with both labeled and unlabeled observations which consists in giving pseudo-labels to unlabeled training instances with a confid…
Semi-supervised Wrapper Feature Selection by Modeling Imperfect Labels
Vasilii Feofanov, Emilie Devijver, Massih-Reza Amini
In this paper, we propose a new wrapper feature selection approach with partially labeled training examples where unlabeled observations are pseudo-labeled using the predictions of…