Smoothness Bias in Relevance Estimators for Feature Selection in Regression

Abstract : Selecting features from high-dimensional datasets is an important problem in machine learning. This paper shows that in the context of filter methods for feature selection, the estimator of the criterion used to select features plays an important role; in particular the estimators may suffer from a bias when comparing smooth and non-smooth features. This paper analyses the origin of such bias and investigates whether this bias influences the results of the feature selection process. Results show that non-smooth features tend to be penalised especially in small datasets.
Document type :
Conference papers
Lazaros Iliadis; Ilias Maglogiannis; Vassilis Plagianakos. 14th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), May 2018, Rhodes, Greece. Springer International Publishing, IFIP Advances in Information and Communication Technology, AICT-519, pp.285-294, 2018, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-319-92007-8_25〉
Liste complète des métadonnées

Cited literature [23 references]  Display  Hide  Download

https://hal.inria.fr/hal-01821055
Contributor : Hal Ifip <>
Submitted on : Friday, June 22, 2018 - 11:45:18 AM
Last modification on : Friday, June 22, 2018 - 12:00:50 PM
Document(s) archivé(s) le : Monday, September 24, 2018 - 10:48:32 AM

File

 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2021-01-01

Please log in to resquest access to the document

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Alexandra Degeest, Michel Verleysen, Benoît Frenay. Smoothness Bias in Relevance Estimators for Feature Selection in Regression. Lazaros Iliadis; Ilias Maglogiannis; Vassilis Plagianakos. 14th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), May 2018, Rhodes, Greece. Springer International Publishing, IFIP Advances in Information and Communication Technology, AICT-519, pp.285-294, 2018, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-319-92007-8_25〉. 〈hal-01821055〉

Share

Metrics

Record views

23