Feature selection for regression problems based on the Morisita estimator of intrinsic dimension

Détails

ID Serval
serval:BIB_42785E14FBE4
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Feature selection for regression problems based on the Morisita estimator of intrinsic dimension
Périodique
Pattern Recognition
Auteur(s)
Golay J., Leuenberger M., Kanevski  M.
ISSN
0031-3203
Statut éditorial
Publié
Date de publication
10/05/2017
Peer-reviewed
Oui
Volume
70
Pages
126-138
Langue
anglais
Résumé
Data acquisition, storage and management have been improved, while the key factors of many phenomena are not well known. Consequently, irrelevant and redundant features artificially increase the size of datasets, which complicates learning tasks, such as regression. To address this problem, feature selection methods have been proposed. This paper introduces a new supervised filter based on the Morisita estimator of intrinsic dimension. It can identify relevant features and distinguish between redundant and irrelevant information. Besides, it offers a clear graphical representation of the results, and it can be easily implemented in different programming languages. Comprehensive numerical experiments are conducted using simulated datasets characterized by different levels of complexity, sample size and noise. The suggested algorithm is also successfully tested on a selection of real world applications and compared with RReliefF using extreme learning machine. In addition, a new measure of feature relevance is presented and discussed.
Mots-clé
Feature selection, Intrinsic dimension, Morisita index, Measure of relevance, Data mining
Création de la notice
10/05/2017 12:08
Dernière modification de la notice
03/03/2018 16:37
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