{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T02:03:58Z","timestamp":1755223438798,"version":"3.43.0"},"reference-count":35,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1109\/tkde.2025.3580671","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T13:40:40Z","timestamp":1750254040000},"page":"5627-5638","source":"Crossref","is-referenced-by-count":0,"title":["Stability of Machine Learning Predictive Features Under Limited Data"],"prefix":"10.1109","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8864-0760","authenticated-orcid":false,"given":"Karol","family":"Capa\u0142a","sequence":"first","affiliation":[{"name":"Sano – Centre for Computational Personalised Medicine, Kraków, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1478-0832","authenticated-orcid":false,"given":"Paulina","family":"Tworek","sequence":"additional","affiliation":[{"name":"Sano – Centre for Computational Personalised Medicine, Kraków, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9570-6054","authenticated-orcid":false,"given":"Jose","family":"Sousa","sequence":"additional","affiliation":[{"name":"Sano – Centre for Computational Personalised Medicine, Kraków, Poland"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107622"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-022-00596-z"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.tics.2020.09.001"},{"key":"ref4","article-title":"Deep learning for low-data drug discovery: Hurdles and opportunities","volume-title":"Curr. Opin. Struct. Biol.","volume":"86","author":"van Tilborg","year":"2024"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1098\/rsif.2017.0376"},{"article-title":"Interpretability and explainability: A machine learning zoo mini-tour","year":"2020","author":"Marcinkevi\u010ds","key":"ref6"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s43681-022-00217-w"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-42941-5_28"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119078"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3649459"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.3390\/app10072344"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.3389\/fdata.2021.688969"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.29172\/7c2a6982-6d72-4cd8-bba6-2fccb06a7011"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1186\/s12870-022-03559-z"},{"key":"ref15","first-page":"507","article-title":"Why do tree-based models still outperform deep learning on typical tabular data?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Grinsztajn"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.2307\/2699986"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref18","first-page":"3149","article-title":"LightGBM: A highly efficient gradient boosting decision tree","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ke"},{"article-title":"Self-supervised learning on graphs: Deep insights and new direction","year":"2020","author":"Jin","key":"ref19"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-09954-8"},{"key":"ref22","article-title":"Enhanced credit card fraud detection based on SVM-recursive feature elimination and hyper-parameters optimization","volume":"55","author":"Naoufal","year":"2020","journal-title":"J. Inf. Secur. Appl."},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.9735\/2229\u20133981"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.abl4747"},{"article-title":"UCI machine learning repository","year":"2017","author":"Dua","key":"ref25"},{"key":"ref26","article-title":"Ionosphere","author":"Sigillito","year":"1989","journal-title":"UCI Mach. Learn. Repository"},{"article-title":"Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository","year":"1988","author":"Sejnowski","key":"ref27"},{"article-title":"Breast cancer wisconsin (Diagnostic)","year":"1993","author":"Wolberg","key":"ref28"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.3390\/s19194342"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms5308"},{"article-title":"Diabetes dataset","year":"2020","author":"Akturk","key":"ref31"},{"article-title":"Pulsar dataset HTRU2","year":"2021","author":"Chugh","key":"ref32"},{"article-title":"Supersymmetric particles data set","year":"2023","author":"Baghcheh","key":"ref33"},{"article-title":"Random forest feature importance computed in 3 ways with python","year":"2020","author":"P\u0142o\u0144ski","key":"ref34"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-10-213"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107734"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/69\/11119623\/11039685.pdf?arnumber=11039685","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T20:50:12Z","timestamp":1754945412000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11039685\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":35,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2025.3580671","relation":{},"ISSN":["1041-4347","1558-2191","2326-3865"],"issn-type":[{"type":"print","value":"1041-4347"},{"type":"electronic","value":"1558-2191"},{"type":"electronic","value":"2326-3865"}],"subject":[],"published":{"date-parts":[[2025,9]]}}}