{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T19:10:35Z","timestamp":1772910635638,"version":"3.50.1"},"reference-count":48,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"DATA Inc. Fellowship","award":["48-AF-W1-2019-00002"],"award-info":[{"award-number":["48-AF-W1-2019-00002"]}]},{"name":"Tecnalia Research and Innovation Ph.D. Scholarship"},{"DOI":"10.13039\/501100001872","name":"Spanish Centro para el Desarrollo Tecnol\u00f3gico Industrial (CDTI, Ministry of Science and Innovation) through the \u201cRed Cervera\u201d Programme","doi-asserted-by":"publisher","award":["CER-20191029"],"award-info":[{"award-number":["CER-20191029"]}],"id":[{"id":"10.13039\/501100001872","id-type":"DOI","asserted-by":"publisher"}]},{"name":"3KIA Project funded by the ELKARTEK Program of the SPRI-Basque Government","award":["KK-2020\/00049"],"award-info":[{"award-number":["KK-2020\/00049"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3110647","type":"journal-article","created":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T20:33:18Z","timestamp":1630960398000},"page":"125462-125477","source":"Crossref","is-referenced-by-count":22,"title":["Normalization Influence on ANN-Based Models Performance: A New Proposal for Features\u2019 Contribution Analysis"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9581-0740","authenticated-orcid":false,"given":"Iratxe","family":"Nino-Adan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1026-3248","authenticated-orcid":false,"given":"Eva","family":"Portillo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Itziar","family":"Landa-Torres","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5656-9042","authenticated-orcid":false,"given":"Diana","family":"Manjarres","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","author":"dua","year":"2017","journal-title":"UCI Machine Learning Repository"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3051049"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2963407"},{"key":"ref32","first-page":"11","article-title":"Investigations on impact of feature normalization techniques on classifier’s performance in breast tumor classification","volume":"116","author":"singh","year":"2015","journal-title":"Int J Comput Appl"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"170","DOI":"10.21449\/ijate.479404","article-title":"The effect of the normalization method used in different sample sizes on the success of artificial neural network model","volume":"6","author":"g\u00f6khan","year":"2019","journal-title":"Int J Assess Tools Educ"},{"key":"ref30","doi-asserted-by":"crossref","first-page":"21","DOI":"10.26634\/jpr.5.3.15678","article-title":"Effects of data normalization on water quality model in a recirculatory aquaculture system using artificial neural network","volume":"5","author":"folorunso","year":"2018","journal-title":"i-manager’s Journal on Pattern Recognition"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3026621"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-020-04943-3"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.3390\/electronics9040692"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.flowmeasinst.2019.101689"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolmodel.2015.04.015"},{"key":"ref40","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-10247-4","volume":"72","author":"garc\u00eda","year":"2015","journal-title":"Data Preprocessing in Data Mining"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/S0304-3800(02)00064-9"},{"key":"ref12","first-page":"306","article-title":"Variance-based feature importance in neural networks","author":"s\u00e1","year":"2019","journal-title":"Proc Int Conf Discovery Sci"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2019.12.012"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-017-1843-1"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/FUZZ-IEEE.2019.8858899"},{"key":"ref16","first-page":"2907","article-title":"Feature relevance quantification in explainable AI: A causal problem","author":"janzing","year":"2020","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-00236-4"},{"key":"ref18","first-page":"721","article-title":"Shapley flow: A graph-based approach to interpreting model predictions","author":"wang","year":"2021","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.2478\/cait-2019-0001"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-004-2434-7"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiia.2020.01.001"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.3390\/fi12060097"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2945545"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmatprotec.2020.116972"},{"key":"ref29","article-title":"Evaluation of feature selection methods based on artificial neural network weights","volume":"168","author":"da costa","year":"2021","journal-title":"Expert Syst Appl"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.compchemeng.2021.107240"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2020.118769"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.3390\/app10165659"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/2.485891"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2870052"},{"key":"ref1","author":"hassoun","year":"1995","journal-title":"Fundamentals of Artificial Neural Networks"},{"key":"ref46","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","author":"glorot","year":"2010","journal-title":"Proc 13th Int Conf Artif Intell Statist"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2018.11.008"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-23485-4_53"},{"key":"ref48","first-page":"1","article-title":"Wilcoxon signed-rank test","author":"woolson","year":"2007","journal-title":"Wiley Encyclopedia of Clinical Trials"},{"key":"ref22","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198538493.001.0001","author":"bishop","year":"1995","journal-title":"Neural Networks for Pattern Recognition"},{"key":"ref47","first-page":"713","author":"puka","year":"2011","journal-title":"Kendall’s Tau"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3007291"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1038\/sj\/jors\/0440106"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty429"},{"key":"ref41","first-page":"46","article-title":"Interpreting neural-network connection weights","volume":"6","author":"garson","year":"1991","journal-title":"AI Expert"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-33-6400-4_4"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.3906\/elk-1807-87"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.12.033"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1177\/1475090214540874"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1002\/er.4682"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09530465.pdf?arnumber=9530465","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T12:10:36Z","timestamp":1699445436000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9530465\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":48,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3110647","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}