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Liknon feature selection for microarrays

Par Conseil national de recherches du Canada

DOITrouver le DOI : https://doi.org/10.1007/978-3-540-73400-0_74
AuteurRechercher : 1; Rechercher : 1
Affiliation
  1. Conseil national de recherches du Canada. Institut du biodiagnostic du CNRC
FormatTexte, Chapitre de livre
Conférence7th International Workshop on Fuzzy Logic and Applications (WILF 2007), July 7-10, 2007. Camogli, Italy
Sujetfeature selection; gene expression microarray; linear programming; support vector machine; LIKNON; regularization parameter; sample to feature ratio
Résumé
Many real-world classification problems involve very sparse and high-dimensional data. The successes of LIKNON - linear programming support vector machine (LPSVM) for feature selection, motivates a more thorough analysis of the method when applied to sparse, multivariate data. Due to the sparseness, the selection of a classification model is greatly influenced by the characteristics of that particular dataset. Robust feature/model selection methods are desirable. LIKNON is claimed to have such robustness properties. Its feature selection operates by selecting the groups of features with large differences between the resultants of the two classes. The degree of desired difference is controlled by the regularization parameter. We study the practical value of LIKNON-based feature/model selection for microarray data. Our findings support the claims about the robustness of the method.
Date de publication
Maison d’éditionSpringer Berlin Heidelberg
Dans
  • Applications of Fuzzy Sets Theory: 7th International Workshop on Fuzzy Logic and Applications, WILF 2007, Camogli, Italy, July 7-10, 2007. Proceedings (2007) : 580–587.
Série
  • Lecture Notes in Computer Science, nº 4578.
Langueanglais
Publications évaluées par des pairsOui
Numéro du CNRCNRC-IBD-2435
Numéro NPARC9148116
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Identificateur de l’enregistrement17253e7d-ff9c-48ca-babe-6071dfd15081
Enregistrement créé2009-06-25
Enregistrement modifié2020-06-17
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