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1. Constraint Selection-Based Semi-supervised Feature Selection.

   Autor*in: Hindawi, Mohammed ; Allab, Kais ; Benabdeslem, Khalid

   Erschienen: 2011

   Verlag:  HAL CCSD ; IEEE

   International audience ; In this paper, we present a novel feature selection approach based on an efficient selection of pair wise constraints. This aims at selecting the most coherent constraints extracted from labeled part of data. The relevance of... mehr

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   Volltext:	https://hal.science/hal-00874960 https://hal.science/hal-00874960/document https://hal.science/hal-00874960/file/Constraint_Selection-Based_Semi-supervised_Feature_Selection.pdf
   Zitierfähiger Link:	https://doi.org/10.1109/ICDM.2011.42

   International audience ; In this paper, we present a novel feature selection approach based on an efficient selection of pair wise constraints. This aims at selecting the most coherent constraints extracted from labeled part of data. The relevance of features is then evaluated according to their efficient locality preserving and chosen constraint preserving ability. Finally, experimental results are provided for validating our proposal with respect to other known feature selection methods.

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   Quelle:	BASE Fachausschnitt Germanistik
   Sprache:	Englisch
   Medientyp:	Konferenzveröffentlichung
   Format:	Online
   Übergeordneter Titel:	ICDM ; https://hal.science/hal-00874960 ; Cook, Diane J. and Pei, Jian and 0010, Wei Wang and Zaïane, Osmar R. and Wu, Xindong. ICDM, Dec 2011, Vancouver,BC, Canada. IEEE, pp.1080-1085, 2011, ⟨10.1109/ICDM.2011.42⟩
   Schlagworte:	feature selection; Dimensionality reduction; constraint selection; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
   Lizenz:	info:eu-repo/semantics/OpenAccess