Bolón-Canedo, VerónicaRego-Fernández, DiegoPeteiro Barral, DiegoAlonso-Betanzos, AmparoGuijarro-Berdiñas, BerthaSánchez-Maroño, Noelia2024-05-032024-05-032018Bolón-Canedo, V., Rego-Fernández, D., Peteiro-Barral, D. et al. On the scalability of feature selection methods on high-dimensional data. Knowl Inf Syst 56, 395–442 (2018). https://doi.org/10.1007/s10115-017-1140-30219-13770219-3116http://hdl.handle.net/2183/36408This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10115-017-1140-3.[Abstract]: Lately, derived from the explosion of high dimensionality, researchers in machine learning became interested not only in accuracy, but also in scalability. Although scalability of learning methods is a trending issue, scalability of feature selection methods has not received the same amount of attention. This research analyzes the scalability of state-of-the-art feature selection methods, belonging to filter, embedded and wrapper approaches. For this purpose, several new measures are presented, based not only on accuracy but also on execution time and stability. The results on seven classical artificial datasets are presented and discussed, as well as two cases study analyzing the particularities of microarray data and the effect of redundancy. Trying to check whether the results can be generalized, we included some experiments with two real datasets. As expected, filters are the most scalable feature selection approach, being INTERACT, ReliefF and mRMR the most accurate methods.engTodos os dereitos reservados. All rights reserved.Copyright © 2017, Springer-Verlag London Ltd., part of Springer NatureFeature selectionScalabilityBig dataHigh-dimensionalityjournal articleopen access10.1007/s10115-017-1140-3