Morán-Fernández, LauraBolón-Canedo, VerónicaAlonso-Betanzos, Amparo2022-03-172022-03-172021MORÁN-FERNÁNDEZ, Laura, BÓLON-CANEDO, Verónica and ALONSO-BETANZOS, Amparo, 2022. How important is data quality? Best classifiers vs best features. Neurocomputing. 22 January 2022. Vol. 470, p. 365–375. DOI 10.1016/j.neucom.2021.05.107http://hdl.handle.net/2183/30051Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] The task of choosing the appropriate classifier for a given scenario is not an easy-to-solve question. First, there is an increasingly high number of algorithms available belonging to different families. And also there is a lack of methodologies that can help on recommending in advance a given family of algorithms for a certain type of datasets. Besides, most of these classification algorithms exhibit a degradation in the performance when faced with datasets containing irrelevant and/or redundant features. In this work we analyze the impact of feature selection in classification over several synthetic and real datasets. The experimental results obtained show that the significance of selecting a classifier decreases after applying an appropriate preprocessing step and, not only this alleviates the choice, but it also improves the results in almost all the datasets tested.engAtribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Feature selectionFiltersPreprocessingHigh dimensionalityClassificationData análisisjournal articleopen access10.1016/j.neucom.2021.05.107