Simple strategies for semi-supervised feature selection

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

zbMATH Keywords

feature selectionsemi-supervisedpositive unlabelled

Mathematics Subject Classification ID

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

• TETRAD

Cites Work

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• 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

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