Constraint scores for semi-supervised feature selection: A comparative study
| Autor: | Ludovic Macaire, Mariam Kalakech, Philippe Biela, Denis Hamad |
|---|---|
| Přispěvatelé: | LAGIS-SI, Laboratoire d'Automatique, Génie Informatique et Signal (LAGIS), Université de Lille, Sciences et Technologies-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Centrale Lille-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique Signal et Image de la Côte d'Opale (LISIC), Université du Littoral Côte d'Opale (ULCO) |
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: |
Feature extraction
Feature selection 02 engineering and technology Machine learning computer.software_genre Signal classification [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] Artificial Intelligence 020204 information systems Scoring algorithm 0202 electrical engineering electronic engineering information engineering Mathematics Signal processing business.industry Pattern recognition Constraint (information theory) ComputingMethodologies_PATTERNRECOGNITION Signal Processing 020201 artificial intelligence & image processing Pairwise comparison Computer Vision and Pattern Recognition Artificial intelligence business computer Laplace operator Software |
| Zdroj: | Pattern Recognition Letters Pattern Recognition Letters, Elsevier, 2011, 32 (5), pp.656-665. ⟨10.1016/j.patrec.2010.12.014⟩ |
| ISSN: | 0167-8655 |
| DOI: | 10.1016/j.patrec.2010.12.014⟩ |
| Popis: | International audience; Recent feature selection scores using pairwise constraints (must-link and cannot-link) have shown better performances than the unsupervised methods and comparable to the supervised ones. However, these scores use only the pairwise constraints and ignore the available information brought by the unlabeled data. Moreover, these constraint scores strongly depend on the given must-link and cannot-link subsets built by the user. In this paper, we address these problems and propose a new semi-supervised constraint score that uses both pairwise constraints and local properties of the unlabeled data. Experiments using Kendall's coefficient and accuracy rates, show that this new score is less sensitive to the given constraints than the previous scores while providing similar performances. |
| Databáze: | OpenAIRE |
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