Cancela, BraisBolón-Canedo, VerónicaAlonso-Betanzos, AmparoGama, João2023-12-012023-12-012019-11Cancela, Brais, et al. «A Scalable Saliency-Based Feature Selection Method with Instance-Level Information». Knowledge-Based Systems, vol. 192, marzo de 2020, p. 105326. https://doi.org/10.1016/j.knosys.2019.105326.0950-7051http://hdl.handle.net/2183/34406© 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article [Cancela, Brais, et al. «A Scalable Saliency-Based Feature Selection Method with Instance-Level Information». Knowledge-Based Systems, vol. 192, marzo de 2020, p. 105326] has been accepted for publication in "Knowledge-Based Systems". The Version of Record is available online at https://doi.org/10.1016/j.knosys.2019.105326.[Abstract]: Classic feature selection techniques remove irrelevant or redundant features to achieve a subset of relevant features in compact models that are easier to interpret and so improve knowledge extraction. Most such techniques operate on the whole dataset, but are unable to provide the user with useful information when only instance-level information is required; in other words, classic feature selection algorithms do not identify the most relevant information in a sample. We have developed a novel feature selection method, called saliency-based feature selection (SFS), based on deep-learning saliency techniques. Our algorithm works under any architecture that is trained by using gradient descent techniques (Neural Networks, SVMs, …), and can be used for classification or regression problems. Experimental results show our algorithm is robust, as it allows to transfer the feature ranking result between different architectures, achieving remarkable results. The versatility of our algorithm has been also demonstrated, as it can work either in big data environments as well as with small datasets.engAtribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/Feature selectionDeep learningSaliencyjournal articleopen access