{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T17:02:24Z","timestamp":1778259744053,"version":"3.51.4"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032045577","type":"print"},{"value":"9783032045584","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T00:00:00Z","timestamp":1757635200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T00:00:00Z","timestamp":1757635200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-04558-4_34","type":"book-chapter","created":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T11:16:46Z","timestamp":1757589406000},"page":"430-441","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Efficient ReliefF: A Low-Power Optimization of\u00a0ReliefF for\u00a0Resource-Constrained Devices"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0228-2678","authenticated-orcid":false,"given":"Samuel","family":"Su\u00e1rez-Marcote","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6703-1846","authenticated-orcid":false,"given":"Laura","family":"Mor\u00e1n-Fern\u00e1ndez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0524-6427","authenticated-orcid":false,"given":"Ver\u00f3nica","family":"Bol\u00f3n-Canedo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,12]]},"reference":[{"key":"34_CR1","unstructured":"Arizona State University. https:\/\/jundongl.github.io\/scikit-feature\/datasets.html. Accessed 28 Sept 2024"},{"issue":"9","key":"34_CR2","doi-asserted-by":"publisher","first-page":"9937","DOI":"10.1109\/TVT.2022.3178612","volume":"71","author":"P Cassar\u00e1","year":"2022","unstructured":"Cassar\u00e1, P., Gotta, A., Valerio, L.: Federated feature selection for cyber-physical systems of systems. IEEE Trans. Veh. Technol. 71(9), 9937\u20139950 (2022)","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"2","key":"34_CR3","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1007\/s10462-018-9614-6","volume":"52","author":"VK Chauhan","year":"2019","unstructured":"Chauhan, V.K., Dahiya, K., Sharma, A.: Problem formulations and solvers in linear SVM: a review. Artif. Intell. Rev. 52(2), 803\u2013855 (2019)","journal-title":"Artif. Intell. Rev."},{"issue":"7","key":"34_CR4","doi-asserted-by":"publisher","first-page":"5113","DOI":"10.1007\/s10462-020-09816-7","volume":"53","author":"T Choudhary","year":"2020","unstructured":"Choudhary, T., Mishra, V., Goswami, A., Sarangapani, J.: A comprehensive survey on model compression and acceleration. Artif. Intell. Rev. 53(7), 5113\u20135155 (2020). https:\/\/doi.org\/10.1007\/s10462-020-09816-7","journal-title":"Artif. Intell. Rev."},{"key":"34_CR5","unstructured":"Gupta, C., et al.: Protonn: compressed and accurate kNN for resource-scarce devices. In: International Conference on Machine Learning, pp. 1331\u20131340. PMLR (2017)"},{"key":"34_CR6","unstructured":"Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L.A.: Feature Extraction: Foundations and Applications, vol.\u00a0207. Springer, Cham (2008)"},{"issue":"5","key":"34_CR7","doi-asserted-by":"publisher","first-page":"1002","DOI":"10.1109\/TAI.2022.3145333","volume":"4","author":"Y Hu","year":"2022","unstructured":"Hu, Y., Zhang, Y., Gong, D., Sun, X.: Multiparticipant federated feature selection algorithm with particle swarm optimization for imbalanced data under privacy protection. IEEE Trans. Arti. Intell. 4(5), 1002\u20131016 (2022)","journal-title":"IEEE Trans. Arti. Intell."},{"issue":"11","key":"34_CR8","doi-asserted-by":"publisher","first-page":"1348","DOI":"10.1016\/j.datak.2009.07.011","volume":"68","author":"Y Huang","year":"2009","unstructured":"Huang, Y., McCullagh, P.J., Black, N.D.: An optimization of reliefF for classification in large datasets. Data Knowl. Eng. 68(11), 1348\u20131356 (2009)","journal-title":"Data Knowl. Eng."},{"key":"34_CR9","unstructured":"Kent Ridge Biomedical Data Set Repository. https:\/\/leo.ugr.es\/elvira\/DBCRepository. Accessed 28 Sept 2024"},{"key":"34_CR10","doi-asserted-by":"crossref","unstructured":"Kira, K., Rendell, L.A.: A practical approach to feature selection. In: Machine Learning Proceedings 1992, pp. 249\u2013256. Elsevier (1992)","DOI":"10.1016\/B978-1-55860-247-2.50037-1"},{"key":"34_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/3-540-57868-4_57","volume-title":"Machine Learning: ECML-94","author":"I Kononenko","year":"1994","unstructured":"Kononenko, I.: Estimating attributes: analysis and extensions of RELIEF. In: Bergadano, F., De Raedt, L. (eds.) ECML 1994. LNCS, vol. 784, pp. 171\u2013182. Springer, Heidelberg (1994). https:\/\/doi.org\/10.1007\/3-540-57868-4_57"},{"key":"34_CR12","unstructured":"Ma, S., et al.: The era of 1-bit llms: all large language models are in 1.58 bits. arXiv preprint arXiv:2402.177641 (2024)"},{"issue":"1","key":"34_CR13","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1002\/widm.53","volume":"2","author":"F Murtagh","year":"2012","unstructured":"Murtagh, F., Contreras, P.: Algorithms for hierarchical clustering: an overview. Wiley Interdisc. Rev. Data Min. Knowl. Discov. 2(1), 86\u201397 (2012)","journal-title":"Wiley Interdisc. Rev. Data Min. Knowl. Discov."},{"issue":"2","key":"34_CR14","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/j.icte.2021.04.012","volume":"7","author":"P Nimbalkar","year":"2021","unstructured":"Nimbalkar, P., Kshirsagar, D.: Feature selection for intrusion detection system in internet-of-things (IoT). ICT Express 7(2), 177\u2013181 (2021)","journal-title":"ICT Express"},{"issue":"19","key":"34_CR15","doi-asserted-by":"publisher","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","volume":"23","author":"Y Saeys","year":"2007","unstructured":"Saeys, Y., Inza, I., Larranaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507\u20132517 (2007)","journal-title":"Bioinformatics"},{"issue":"1","key":"34_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TKDE.2011.181","volume":"25","author":"Q Song","year":"2011","unstructured":"Song, Q., Ni, J., Wang, G.: A fast clustering-based feature subset selection algorithm for high-dimensional data. IEEE Trans. Knowl. Data Eng. 25(1), 1\u201314 (2011)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"7\u20138","key":"34_CR17","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1016\/j.ijmedinf.2005.05.002","volume":"74","author":"A Statnikov","year":"2005","unstructured":"Statnikov, A., Tsamardinos, I., Dosbayev, Y., Aliferis, C.F.: Gems: a system for automated cancer diagnosis and biomarker discovery from microarray gene expression data. Int. J. Med. Inform. 74(7\u20138), 491\u2013503 (2005)","journal-title":"Int. J. Med. Inform."},{"key":"34_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.128099","volume":"596","author":"S Su\u00e1rez-Marcote","year":"2024","unstructured":"Su\u00e1rez-Marcote, S., Mor\u00e1n-Fern\u00e1ndez, L., Bol\u00f3n-Canedo, V.: Towards federated feature selection: logarithmic division for resource-conscious methods. Neurocomputing 596, 128099 (2024)","journal-title":"Neurocomputing"},{"key":"34_CR19","unstructured":"Sun, X., et al.: Hybrid 8-bit floating point (HFP8) training and inference for deep neural networks. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"34_CR20","unstructured":"University of California, Irvine, School of Information and Computer Sciences. http:\/\/archive.ics.uci.edu\/ml. Accessed 27 Sept 2024"},{"key":"34_CR21","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.jbi.2018.07.014","volume":"85","author":"RJ Urbanowicz","year":"2018","unstructured":"Urbanowicz, R.J., Meeker, M., La Cava, W., Olson, R.S., Moore, J.H.: Relief-based feature selection: introduction and review. J. Biomed. Inform. 85, 189\u2013203 (2018)","journal-title":"J. Biomed. Inform."},{"key":"34_CR22","doi-asserted-by":"crossref","unstructured":"Varghese, B., Wang, N., Barbhuiya, S., Kilpatrick, P., Nikolopoulos, D.S.: Challenges and opportunities in edge computing. In: 2016 IEEE International Conference on Smart Cloud (SmartCloud), pp. 20\u201326. IEEE (2016)","DOI":"10.1109\/SmartCloud.2016.18"},{"key":"34_CR23","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1007\/s10766-018-00624-9","volume":"47","author":"Y Yu","year":"2019","unstructured":"Yu, Y., Zhi, T., Zhou, X., Liu, S., Chen, Y., Cheng, S.: BSHIFT: a low cost deep neural networks accelerator. Int. J. Parallel Prog. 47, 360\u2013372 (2019)","journal-title":"Int. J. Parallel Prog."}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04558-4_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T11:16:49Z","timestamp":1757589409000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04558-4_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,12]]},"ISBN":["9783032045577","9783032045584"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04558-4_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,12]]},"assertion":[{"value":"12 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kaunas","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"34","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}