Simple strategies for semi-supervised feature selection
From MaRDI portal
Publication:1707485
DOI10.1007/s10994-017-5648-2zbMath1457.68239OpenAlexW2623012606WikidataQ92952701 ScholiaQ92952701MaRDI QIDQ1707485
Konstantinos Sechidis, Gavin Brown
Publication date: 3 April 2018
Published in: Machine Learning (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10994-017-5648-2
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)
Related Items (5)
Joint feature selection and classification for positive unlabelled multi-label data using weighted penalized empirical risk minimization ⋮ A user-guided Bayesian framework for ensemble feature selection in life science applications (UBayFS) ⋮ Graph-based semi-supervised learning via improving the quality of the graph dynamically ⋮ Efficient feature selection using shrinkage estimators ⋮ Learning from positive and unlabeled data: a survey
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Semi-supervised learning of class balance under class-prior change by distribution matching
- BASSUM: a Bayesian semi-supervised method for classification feature selection
- Machine learning with squared-loss mutual information
- Feature extraction. Foundations and applications. Papers from NIPS 2003 workshop on feature extraction, Whistler, BC, Canada, December 11--13, 2003. With CD-ROM.
- Graphical models for inference under outcome-dependent sampling
- Causation, prediction, and search
- Could Fisher, Jeffreys and Neyman have agreed on testing? (With comments and a rejoinder).
- A survey on semi-supervised feature selection methods
- Dealing with under-reported variables: an information theoretic solution
- Pearson's X 2 and the Loglikelihood Ratio Statistic G 2 : A Comparative Review
- A discriminative model for semi-supervised learning
- Elements of Information Theory
This page was built for publication: Simple strategies for semi-supervised feature selection