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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.
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Submitted on : Friday, June 22, 2018 - 11:45:18 AM
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Alexandra Degeest, Michel Verleysen, Benoît Frenay. Smoothness Bias in Relevance Estimators for Feature Selection in Regression. 14th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), May 2018, Rhodes, Greece. pp.285-294, ⟨10.1007/978-3-319-92007-8_25⟩. ⟨hal-01821055⟩



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