{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T17:24:56Z","timestamp":1774718696757,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685489","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"<jats:p>Sparse Neural Networks (SNNs) have emerged as powerful tools for efficient feature selection. Leveraging the dynamic sparse training (DST) algorithms within SNNs has demonstrated promising feature selection capabilities while drastically reducing computational overheads. Despite these advancements, several critical aspects remain insufficiently explored for feature selection. Questions persist regarding the choice of the DST algorithm for network training, the choice of metric for ranking features\/neurons, and the comparative performance of these methods across diverse datasets when compared to dense networks. This paper addresses these gaps by presenting a comprehensive systematic analysis of feature selection with sparse neural networks. Moreover, we introduce a novel metric considering sparse neural network characteristics, which is designed to quantify feature importance within the context of SNNs. Our findings show that feature selection with SNNs trained with DST algorithms can achieve, on average, more than 50% memory and 55% FLOPs reduction compared to the dense networks, while outperforming them in terms of the quality of the selected features. Our code and the supplementary material are available on GitHub (https:\/\/github.com\/zahraatashgahi\/Neuron-Attribution).<\/jats:p>","DOI":"10.3233\/faia240799","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:24:25Z","timestamp":1729171465000},"source":"Crossref","is-referenced-by-count":5,"title":["Unveiling the Power of Sparse Neural Networks for Feature Selection"],"prefix":"10.3233","author":[{"given":"Zahra","family":"Atashgahi","sequence":"first","affiliation":[{"name":"Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tennison","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics and Theoretical Physics, University of Cambridge, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mykola","family":"Pechenizkiy","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, Eindhoven University of Technology, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raymond","family":"Veldhuis","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Decebal Constantin","family":"Mocanu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Luxembourg, Luxembourg"},{"name":"Department of Mathematics and Computer Science, Eindhoven University of Technology, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mihaela","family":"van der Schaar","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics and Theoretical Physics, University of Cambridge, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240799","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:24:26Z","timestamp":1729171466000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240799"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240799","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}