{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T04:12:29Z","timestamp":1773979949558,"version":"3.50.1"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T00:00:00Z","timestamp":1765843200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T00:00:00Z","timestamp":1765843200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Ministry of Higher Education","award":["FRGS\/1\/2023\/ICT02\/USM\/02\/2"],"award-info":[{"award-number":["FRGS\/1\/2023\/ICT02\/USM\/02\/2"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1007\/s10115-025-02616-x","type":"journal-article","created":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T14:44:59Z","timestamp":1765896299000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Iterative feature exclusion ranking for deep tabular learning"],"prefix":"10.1007","volume":"68","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2002-7011","authenticated-orcid":false,"given":"Fathi Said Emhemed","family":"Shaninah","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5777-7694","authenticated-orcid":false,"given":"AbdulRahman M. A.","family":"Baraka","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3300-3270","authenticated-orcid":false,"given":"Mohd Halim Mohd","family":"Noor","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,12,16]]},"reference":[{"key":"2616_CR1","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2020.0110477","author":"S Shabudin","year":"2020","unstructured":"Shabudin S, Sani NS, Ariffin KAZ, Aliff M (2020) Feature selection for phishing website classification. Int J Adv Comput Sci Appl 11(4). https:\/\/doi.org\/10.14569\/IJACSA.2020.0110477","journal-title":"Int J Adv Comput Sci Appl"},{"issue":"1","key":"2616_CR2","doi-asserted-by":"publisher","first-page":"239","DOI":"10.12785\/ijcds\/150119","volume":"15","author":"F Alshaikh","year":"2024","unstructured":"Alshaikh F, Hewahi N (2024) Convolutional neural network for predicting student academic performance in intelligent tutoring system. Int J Comput Digit Syst 15(1):239\u2013258","journal-title":"Int J Comput Digit Syst"},{"issue":"1","key":"2616_CR3","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-26956-8","volume":"13","author":"F Ahmad","year":"2023","unstructured":"Ahmad F, Ghani Khan MU, Tahir A, Tipu MY, Rabbani M, Shabbir MZ (2023) Two phase feature-ranking for new soil dataset for Coxiella burnetii persistence and classification using machine learning models. Sci Rep 13(1):29","journal-title":"Sci Rep"},{"key":"2616_CR4","unstructured":"Ren W, Zhao T, Huang Y, Honavar V (2025) Deep learning within tabular data: foundations, challenges, advances and future directions [Online]. arXiv:2501.03540"},{"key":"2616_CR5","unstructured":"Cartella F, Anunciacao O, Funabiki Y, Yamaguchi D, Akishita T, Elshocht O (2021) Adversarial attacks for tabular data: Application to fraud detection and imbalanced data. arXiv preprint arXiv:2101.08030"},{"key":"2616_CR6","first-page":"5105","volume":"33","author":"M Wojtas","year":"2020","unstructured":"Wojtas M, Chen K (2020) Feature importance ranking for deep learning. Adv Neural Inf Process Syst 33:5105\u20135114","journal-title":"Adv Neural Inf Process Syst"},{"key":"2616_CR7","unstructured":"Tabachnick BG, Fidell LS (2007) Experimental designs using ANOVA. Thomson\/Brooks\/Cole"},{"issue":"6","key":"2616_CR8","doi-asserted-by":"publisher","first-page":"941","DOI":"10.1016\/j.rcl.2021.06.005","volume":"59","author":"J Kalpathy-Cramer","year":"2021","unstructured":"Kalpathy-Cramer J, Patel JB, Bridge C, Chang K (2021) Basic Artificial Intelligence Techniques: evaluation of artificial intelligence performance. Radiol Clin North Am 59(6):941\u2013954. https:\/\/doi.org\/10.1016\/j.rcl.2021.06.005","journal-title":"Radiol Clin North Am"},{"key":"2616_CR9","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.5154947","author":"X Cheng","year":"2025","unstructured":"Cheng X (2025) A comprehensive study of feature selection techniques in machine learning models. SSRN Electron J. https:\/\/doi.org\/10.2139\/ssrn.5154947","journal-title":"SSRN Electron J"},{"key":"2616_CR10","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-023-00694-8","author":"Y Yin","year":"2023","unstructured":"Yin Y et al (2023) IGRF-RFE: a hybrid feature selection method for MLP-based network intrusion detection on UNSW-NB15 dataset. J Big Data. https:\/\/doi.org\/10.1186\/s40537-023-00694-8","journal-title":"J Big Data"},{"key":"2616_CR11","doi-asserted-by":"publisher","unstructured":"Nnadi LC, Watanobe Y, Rahman MM, John-Otumu AM (2024) Prediction of students\u2019 adaptability using explainable AI in educational machine learning models. https:\/\/doi.org\/10.20944\/preprints202405.0933.v1","DOI":"10.20944\/preprints202405.0933.v1"},{"issue":"11","key":"2616_CR12","doi-asserted-by":"publisher","first-page":"4379","DOI":"10.1007\/s10994-022-06181-0","volume":"112","author":"M Petkovi\u0107","year":"2023","unstructured":"Petkovi\u0107 M, D\u017eeroski S, Kocev D (2023) Feature ranking for semi-supervised learning. Mach Learn 112(11):4379\u20134408","journal-title":"Mach Learn"},{"key":"2616_CR13","unstructured":"Ruggieri S (2019) Complete search for feature selection in decision trees [Online]. http:\/\/jmlr.org\/papers\/v20\/18-035.html"},{"issue":"2","key":"2616_CR14","doi-asserted-by":"publisher","first-page":"623","DOI":"10.17559\/TV-20220823104912","volume":"30","author":"Z Wang","year":"2023","unstructured":"Wang Z (2023) Research on feature selection methods based on random forest. Teh Vjesn 30(2):623\u2013633. https:\/\/doi.org\/10.17559\/TV-20220823104912","journal-title":"Teh Vjesn"},{"key":"2616_CR15","unstructured":"Xu ZE, Huang G, Weinberger KQ, Zheng AX (2019) Gradient boosted feature selection [Online]. arXiv:1901.04055"},{"key":"2616_CR16","unstructured":"Gorishniy Y, Rubachev I, Khrulkov V, Babenko A. Revisiting deep learning models for tabular data [Online]. https:\/\/github.com\/yandex-research\/rtdl"},{"issue":"2","key":"2616_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3429445","volume":"15","author":"Z Zhou","year":"2021","unstructured":"Zhou Z, Hooker G (2021) Unbiased measurement of feature importance in tree-based methods. ACM Trans Knowl Discov Data (TKDD) 15(2):1\u201321. https:\/\/doi.org\/10.1145\/3429445","journal-title":"ACM Trans Knowl Discov Data (TKDD)"},{"key":"2616_CR18","unstructured":"Joseph M, Raj H (2022) GANDALF: gated adaptive network for deep automated learning of features [Online]. arXiv:2207.08548"},{"key":"2616_CR19","doi-asserted-by":"crossref","unstructured":"Primo\u017ei\u010d U, \u0160krlj B, D\u017eeroski S, Petkovi\u0107 M (2021) Unsupervised feature ranking via attribute networks. In: International conference on discovery science. Springer, pp 334\u2013343","DOI":"10.1007\/978-3-030-88942-5_26"},{"key":"2616_CR20","doi-asserted-by":"publisher","unstructured":"\u0160krlj B, D\u017eeroski S, Lavra\u010d N, Petkovi\u010d M (2020) Feature importance estimation with self-attention networks. https:\/\/doi.org\/10.3233\/FAIA200256","DOI":"10.3233\/FAIA200256"},{"key":"2616_CR21","doi-asserted-by":"publisher","unstructured":"Zhang X, Wang Z, Jiang L, Gao W, Wang P, Liu K (2024) TFWT: tabular feature weighting with transformer. In: Larson K (ed) Proceedings of the thirty-third international joint conference on artificial intelligence, IJCAI-24 (pp 2570\u20132578). International Joint Conferences on Artificial Intelligence Organization. https:\/\/doi.org\/10.24963\/ijcai.2024\/284","DOI":"10.24963\/ijcai.2024\/284"},{"key":"2616_CR22","doi-asserted-by":"publisher","unstructured":"Liu D, et al (2023) DIWIFT: discovering instance-wise influential features for tabular data. In: ACM web conference 2023\u2014proceedings of the World Wide Web Conference, WWW 2023. Association for Computing Machinery, Inc, pp 1673\u20131682. https:\/\/doi.org\/10.1145\/3543507.3583382","DOI":"10.1145\/3543507.3583382"},{"key":"2616_CR23","unstructured":"Prabha Appadurai D, Polsani P, Kadari S, Bhaskar Reddy Y, Kishore V (2024) Interpretable machine learning models with attention-based feature attribution for high-dimensional tabular data [Online]. https:\/\/www.jisem-journal.com\/"},{"key":"2616_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.111084","volume":"281","author":"Y Xue","year":"2023","unstructured":"Xue Y, Zhang C, Neri F, Gabbouj M, Zhang Y (2023) An external attention-based feature ranker for large-scale feature selection. Knowl Based Syst 281:111084","journal-title":"Knowl Based Syst"},{"key":"2616_CR25","unstructured":"Yasuda T, Bateni M, Chen L, Fahrbach M, Fu G, Mirrokni V (2022) Sequential attention for feature selection. arXiv preprint arXiv:2209.14881"},{"key":"2616_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2025.113700","author":"C Guo","year":"2025","unstructured":"Guo C, Wang X, Huang C, Wang Y, Gao C, Huang X (2025) Multi-label feature selection via exploring reliable instance similarities. Knowl Based Syst. https:\/\/doi.org\/10.1016\/j.knosys.2025.113700","journal-title":"Knowl Based Syst"},{"issue":"5","key":"2616_CR27","doi-asserted-by":"publisher","first-page":"2606","DOI":"10.1109\/TNNLS.2021.3107049","volume":"34","author":"D Vlahek","year":"2021","unstructured":"Vlahek D, Mongus D (2021) An efficient iterative approach to explainable feature learning. IEEE Trans Neural Netw Learn Syst 34(5):2606\u20132618","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2616_CR28","doi-asserted-by":"publisher","unstructured":"Hao M (2024) WA_Fn-UseC_-telco-customer-churn. IEEE Dataport. https:\/\/doi.org\/10.21227\/0q5y-3529","DOI":"10.21227\/0q5y-3529"},{"issue":"4","key":"2616_CR29","first-page":"1265","volume":"17","author":"V Amarnadh","year":"2023","unstructured":"Amarnadh V, Moparthi NR (2023) Comprehensive review of different artificial intelligence-based methods for credit risk assessment in data science. Intell Decis Technol 17(4):1265\u20131282","journal-title":"Intell Decis Technol"},{"key":"2616_CR30","unstructured":"Frank TC, Harrell Jr. E (2017) Titanic dataset. https:\/\/www.openml.org\/d\/40945 [Online]"},{"key":"2616_CR31","doi-asserted-by":"publisher","unstructured":"Becker B, Kohavi R (1996) Adult. UCI Machine Learning Repository. Accessed 19 Sep 2025 [Online]. https:\/\/doi.org\/10.24432\/C5XW20","DOI":"10.24432\/C5XW20"},{"key":"2616_CR32","unstructured":"Cortez P, Silva AMG (2008) Using data mining to predict secondary school student performance. In: Brito A, Teixeira J (eds) Proceedings of 5th annual future business technology conference, Porto, pp 5\u201312"},{"key":"2616_CR33","doi-asserted-by":"crossref","unstructured":"Akiba T, Sano S, Yanase T, Ohta T, Koyama M (2019) Optuna: a next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 2623\u20132631","DOI":"10.1145\/3292500.3330701"},{"key":"2616_CR34","unstructured":"V MJ, et al. (2024) manujosephv\/pytorch_tabular: v1.1.0. Zenodo. 10.5281\/zenodo.10513095"},{"key":"2616_CR35","unstructured":"Lundberg S, Lee S-I (2017) A unified approach to interpreting model predictions [Online]. arXiv:1705.07874"},{"key":"2616_CR36","doi-asserted-by":"crossref","unstructured":"Deeva I, Kropacheva A (2025) To select or not to select? The role of meta-features selection in meta-learning tasks with tabular data. In: W and CSA and SY and AD and DJJ and SPMA. Lees Michael H and Cai (eds) Computational Science\u2014ICCS 2025. Springer, Cham, pp 294\u2013308","DOI":"10.1007\/978-3-031-97629-2_21"},{"key":"2616_CR37","unstructured":"Joseph M, Raj H (2022) GANDALF: gated adaptive network for deep automated learning of features. arXiv preprint arXiv:2207.08548"},{"key":"2616_CR38","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.inffus.2021.11.011","volume":"81","author":"R Shwartz-Ziv","year":"2022","unstructured":"Shwartz-Ziv R, Armon A (2022) Tabular data: deep learning is not all you need. Inf Fusion 81:84\u201390","journal-title":"Inf Fusion"},{"key":"2616_CR39","unstructured":"Popov S, Morozov S, Babenko A (2019) Neural oblivious decision ensembles for deep learning on tabular data [Online]. arXiv:1909.06312"},{"key":"2616_CR40","unstructured":"Sercan S, Ar\u0131k S, Pfister T (2021) TabNet: attentive interpretable tabular learning [Online]. http:\/\/www.aaai.org"},{"key":"2616_CR41","doi-asserted-by":"crossref","unstructured":"Chen J, Liao K, Wan Y, Chen DZ, Wu J (2021) DANets: deep abstract networks for tabular data classification and regression [Online]. arXiv:2112.02962","DOI":"10.1609\/aaai.v36i4.20309"},{"key":"2616_CR42","unstructured":"Fisher A, Rudin C, Dominici F (2018) All models are wrong, but many are useful: learning a variable\u2019s importance by studying an entire class of prediction models simultaneously [Online]. arXiv:1801.01489"},{"key":"2616_CR43","unstructured":"Picard D (2023) Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision [Online]. arXiv:2109.08203"},{"key":"2616_CR44","doi-asserted-by":"crossref","unstructured":"Shaninah FSES, Noor MHM (2023) The impact of big five personality trait in predicting student academic performance. J Appl Res High Educ. https:\/\/api.semanticscholar.org\/CorpusID:259873232","DOI":"10.1108\/JARHE-08-2022-0274"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-025-02616-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-025-02616-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-025-02616-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T14:45:14Z","timestamp":1765896314000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-025-02616-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,16]]},"references-count":44,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["2616"],"URL":"https:\/\/doi.org\/10.1007\/s10115-025-02616-x","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,16]]},"assertion":[{"value":"6 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 November 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 November 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"5"}}