@inproceedings{e876a2ca587d4595b66a9e215301d998,
title = "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.",
keywords = "Feature selection, Filter methods, Mutual information, Noise variance, Smoothness",
author = "Alexandra Degeest and Michel Verleysen and Beno{\^i}t Fr{\'e}nay",
year = "2018",
month = jan,
day = "1",
doi = "10.1007/978-3-319-92007-8_25",
language = "English",
isbn = "9783319920061",
series = "IFIP Advances in Information and Communication Technology",
publisher = "Springer New York",
pages = "285--294",
editor = "Ilias Maglogiannis and Lazaros Iliadis and Vassilis Plagianakos",
booktitle = "Artificial Intelligence Applications and Innovations - 14th IFIP WG 12.5 International Conference, AIAI 2018, Proceedings",
address = "United States",
note = "14th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2018 ; Conference date: 25-05-2018 Through 27-05-2018",
}