Semi supervised relevance learning for feature selection on high dimensional data

Ben Brahim, Afef (Université de Tunis, Tunis Business School, Tunisia) ; Kalousis, Alexandros (Haute école de gestion de Genève, HES-SO // Haute Ecole Spécialisée de Suisse Occidentale ; University of Geneva, Switzerland)

Nowadays, the advanced technologies make amounts of data growing in a fast paced way. In many application fields, this trend concerns specially dimensions of the data. It is the case where features are about thousands and tens of thousands, while the number of instances is much smaller. This phenomenon is known as the curse of dimensionality and it results in modest classification performance and feature selection instability. In order to deal with this issue, we propose a new feature selection approach that makes use of background knowledge about some dimensions known to be more relevant, as a means of directing the feature selection process. In this approach, prior knowledge about some features is used to learn new relevant features by a semi supervised approach. Experiments on three high dimensional data sets show promising results on both classification performance and stability of feature selection.

Type de conférence:
full paper
Economie et Services
HEG - Genève
CRAG - Centre de Recherche Appliquée en Gestion
Adresse bibliogr.:
Hammamet, Tunisia, 30 October - 3 November 2017
Hammamet, Tunisia
30 October - 3 November 2017
6 p.
Veröffentlicht in:
Proceedings of 14th ACS/IEEE International Conference on Computer Systems and Applications AICCSA 2017
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 Datensatz erzeugt am 2018-10-04, letzte Änderung am 2020-03-25

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