{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T08:30:31Z","timestamp":1765960231584,"version":"3.37.3"},"reference-count":69,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T00:00:00Z","timestamp":1655856000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T00:00:00Z","timestamp":1655856000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["TAILOR: grant No. 952215"],"award-info":[{"award-number":["TAILOR: grant No. 952215"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004329","name":"Javna Agencija za Raziskovalno Dejavnost RS","doi-asserted-by":"publisher","award":["P2-0103","J7-9400"],"award-info":[{"award-number":["P2-0103","J7-9400"]}],"id":[{"id":"10.13039\/501100004329","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004329","name":"Javna Agencija za Raziskovalno Dejavnost RS","doi-asserted-by":"publisher","award":["J7-1815","N2-0128"],"award-info":[{"award-number":["J7-1815","N2-0128"]}],"id":[{"id":"10.13039\/501100004329","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004329","name":"Javna Agencija za Raziskovalno Dejavnost RS","doi-asserted-by":"publisher","award":["J2-9230"],"award-info":[{"award-number":["J2-9230"]}],"id":[{"id":"10.13039\/501100004329","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2023,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The data used for analysis are becoming increasingly complex along several directions: high dimensionality, number of examples and availability of labels for the examples. This poses a variety of challenges for the existing machine learning methods, related to analyzing datasets with a large number of examples that are described in a high-dimensional space, where not all examples have labels provided. For example, when investigating the toxicity of chemical compounds, there are many compounds available that can be described with information-rich high-dimensional representations, but not all of the compounds have information on their toxicity. To address these challenges, we propose methods for semi-supervised learning (SSL) of feature rankings. The feature rankings are learned in the context of classification and regression, as well as in the context of structured output prediction (multi-label classification, MLC, hierarchical multi-label classification, HMLC and multi-target regression, MTR) tasks. This is the first work that treats the task of feature ranking uniformly across various tasks of semi-supervised structured output prediction. To the best of our knowledge, it is also the first work on SSL of feature rankings for the tasks of HMLC and MTR. More specifically, we propose two approaches\u2014based on predictive clustering tree ensembles and the Relief family of algorithms\u2014and evaluate their performance across 38 benchmark datasets. The extensive evaluation reveals that rankings based on Random Forest ensembles perform the best for classification tasks (incl. MLC and HMLC tasks) and are the fastest for all tasks, while ensembles based on extremely randomized trees work best for the regression tasks. Semi-supervised feature rankings outperform their supervised counterparts across the majority of datasets for all of the different tasks, showing the benefit of using unlabeled in addition to labeled data.<\/jats:p>","DOI":"10.1007\/s10994-022-06181-0","type":"journal-article","created":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T20:30:02Z","timestamp":1655929802000},"page":"4379-4408","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Feature ranking for semi-supervised learning"],"prefix":"10.1007","volume":"112","author":[{"given":"Matej","family":"Petkovi\u0107","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sa\u0161o","family":"D\u017eeroski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0687-0878","authenticated-orcid":false,"given":"Dragi","family":"Kocev","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,6,22]]},"reference":[{"issue":"1","key":"6181_CR1","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1007\/s10115-015-0841-8","volume":"47","author":"A Alalga","year":"2016","unstructured":"Alalga, A., Benabdeslem, K., & Taleb, N. (2016). Soft-constrained Laplacian score for semi-supervised multi-label feature selection. Knowledge and Information Systems, 47(1), 75\u201398.","journal-title":"Knowledge and Information Systems"},{"key":"6181_CR2","unstructured":"Arthur, D., & Vassilvitskii, S. (2007). K-means++: The advantages of careful seeding. In Proceedings of the eighteenth annual ACM-SIAM symposium on discrete algorithms, SODA \u201907 (pp. 1027\u20131035), USA. Society for Industrial and Applied Mathematics."},{"issue":"10","key":"6181_CR3","doi-asserted-by":"crossref","first-page":"1426","DOI":"10.1016\/j.patrec.2012.03.001","volume":"33","author":"F Bellal","year":"2012","unstructured":"Bellal, F., Elghazel, H., & Aussem, A. (2012). A semi-supervised feature ranking method with ensemble learning. Pattern Recognition Letters, 33(10), 1426\u20131433.","journal-title":"Pattern Recognition Letters"},{"key":"6181_CR4","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.isprsjprs.2018.02.005","volume":"138","author":"K Bhardwaj","year":"2018","unstructured":"Bhardwaj, K., & Patra, S. (2018). An unsupervised technique for optimal feature selection in attribute profiles for spectral-spatial classification of hyperspectral images. ISPRS Journal of Photogrammetry and Remote Sensing, 138, 139\u2013150.","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"key":"6181_CR5","unstructured":"Bilken University. (2020). Function approximation repository. Accessible at http:\/\/funapp.cs.bilkent.edu.tr\/DataSets\/."},{"key":"6181_CR6","doi-asserted-by":"crossref","unstructured":"Blockeel, H. (1998). Top-down induction of first order logical decision trees. PhD thesis, Katholieke Universiteit Leuven, Leuven, Belgium.","DOI":"10.1016\/S0004-3702(98)00034-4"},{"issue":"9","key":"6181_CR7","doi-asserted-by":"crossref","first-page":"1757","DOI":"10.1016\/j.patcog.2004.03.009","volume":"37","author":"MR Boutell","year":"2004","unstructured":"Boutell, M. R., Luo, J., Shen, X., & Brown, C. M. (2004). Learning multi-label scene classification. Pattern Recognition, 37(9), 1757\u20131771.","journal-title":"Pattern Recognition"},{"issue":"1","key":"6181_CR8","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5\u201332.","journal-title":"Machine Learning"},{"key":"6181_CR9","doi-asserted-by":"crossref","unstructured":"Briggs, F., Huang, Y., Raich, R., Eftaxias, K., Lei, Z., Cukierski, W., Frey Hadley, S., Hadley, A., Betts, M., Fern, X. Z., Irvine, J., Neal, L., Thomas, A., Fodor, G., Tsoumakas, G., Ng Hong, W., Nguyen, T. N. T., Huttunen, H., Ruusuvuori, P., ... Milakov, M. (2013). The 9th annual mlsp competition: New methods for acoustic classification of multiple simultaneous bird species in a noisy environment. In IEEE international workshop on machine learning for signal processing, MLSP, 2013 (pp. 1\u20138).","DOI":"10.1109\/MLSP.2013.6661934"},{"key":"6181_CR10","doi-asserted-by":"crossref","unstructured":"Chang, X., Nie, F., Yang, Y., & Huang, H. (2014a). A convex formulation for semi-supervised multi-label feature selection. In Proceedings of the twenty-eighth AAAI conference on artificial intelligence, AAAI\u201914 (pp. 1171\u20131177). AAAI Press.","DOI":"10.1609\/aaai.v28i1.8922"},{"key":"6181_CR11","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1007\/978-3-319-06605-9_7","volume-title":"Advances in knowledge discovery and data mining. Lecture notes in computer science","author":"X Chang","year":"2014","unstructured":"Chang, X., Shen, H., Wang, S., Liu, J., & Li, X. (2014b). Semi-supervised feature analysis for multimedia annotation by mining label correlation. In V. S. Tseng, B. T. Ho, Z.-H. Zhou, A. L. P. Chen, & H. Kao (Eds.), Advances in knowledge discovery and data mining. Lecture notes in computer science (pp. 74\u201385). Berlin: Springer."},{"issue":"4","key":"6181_CR12","doi-asserted-by":"crossref","first-page":"1821","DOI":"10.1109\/TPWRS.2004.835679","volume":"19","author":"B-J Chen","year":"2004","unstructured":"Chen, B.-J., Chang, M.-W., & Lin, C.-J. (2004). Load forecasting using support vector machines: A study on EUNITE competition 2001. IEEE Transactions on Power Systems, 19(4), 1821\u20131830.","journal-title":"IEEE Transactions on Power Systems"},{"key":"6181_CR13","unstructured":"Clare, A. (2003). Machine learning and data mining for yeast functional genomics. PhD thesis, University of Wales Aberystwyth, Aberystwyth, Wales, UK."},{"key":"6181_CR14","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.ecolmodel.2005.08.017","volume":"191","author":"D Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar, D., D\u017eeroski, S., Larsen, T., Struyf, J., Axelsen, J., Bruus, M., & Krogh, P. H. (2006). Using multi-objective classification to model communities of soil microarthropods. Ecological Modelling, 191, 131\u2013143.","journal-title":"Ecological Modelling"},{"issue":"2","key":"6181_CR15","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/S0167-6296(02)00126-1","volume":"22","author":"JA DiMasi","year":"2003","unstructured":"DiMasi, J. A., Hansen, R. W., & Grabowski, H. G. (2003). The price of innovation: New estimates of drug development costs. Journal of Health Economics, 22(2), 151\u2013185.","journal-title":"Journal of Health Economics"},{"key":"6181_CR16","unstructured":"Dimitrovski, I., Kocev, D., Loskovska, S., & D\u017eeroski, S. (2008). Hierchical annotation of medical images. In Proceedings of the 11th international multiconference: Information Society IS 2008 (pp. 174\u2013181). IJS, Ljubljana."},{"key":"6181_CR17","doi-asserted-by":"crossref","unstructured":"Diplaris, S., Tsoumakas, G., Mitkas, P., & Vlahavas, I. (2005). Protein classification with multiple algorithms. In 10th Panhellenic conference on informatics (PCI 2005) (pp. 448\u2013456).","DOI":"10.1007\/11573036_42"},{"key":"6181_CR18","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.neucom.2012.10.028","volume":"121","author":"G Doquire","year":"2013","unstructured":"Doquire, G., & Verleysen, M. (2013). A graph Laplacian based approach to semi-supervised feature selection for regression problems. Neurocomputing, 121, 5\u201313.","journal-title":"Neurocomputing"},{"key":"6181_CR19","doi-asserted-by":"crossref","unstructured":"D\u017eeroski, S., Potamias, G., Moustakis, V., & Charissis, G. (1997). Automated revision of expert rules for treating acute abdominal pain in children. In Proceedings of the 6th conference on artificial intelligence in medicine in Europe, AIME \u201997 (pp. 98\u2013109). Berlin: Springer.","DOI":"10.1007\/BFb0029440"},{"key":"6181_CR20","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.envsoft.2014.08.015","volume":"62","author":"S Galelli","year":"2014","unstructured":"Galelli, S., Humphrey, G. B., Maier, H. R., Castelletti, A., Dandy, G. C., & Gibbs, M. S. (2014). An evaluation framework for input variable selection algorithms for environmental data-driven models. Environmental Modelling & Software, 62, 33\u201351.","journal-title":"Environmental Modelling & Software"},{"issue":"1","key":"6181_CR21","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","volume":"36","author":"P Geurts","year":"2006","unstructured":"Geurts, P., Erns, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 36(1), 3\u201342.","journal-title":"Machine Learning"},{"key":"6181_CR22","doi-asserted-by":"crossref","unstructured":"Gharroudi, O., Elghazel, H., & Aussem, A. (2016). A semi-supervised ensemble approach for multi-label learning. In 2016 IEEE 16th international conference on data mining workshops (ICDMW) (pp. 1197\u20131204).","DOI":"10.1109\/ICDMW.2016.0173"},{"key":"6181_CR23","unstructured":"Gijsbers, P. (2017). Dis data. Retrieved from OpenML repository https:\/\/www.openml.org\/d\/40713."},{"key":"6181_CR24","doi-asserted-by":"crossref","first-page":"30","DOI":"10.3389\/fmolb.2016.00030","volume":"3","author":"D Grissa","year":"2016","unstructured":"Grissa, D., P\u00e9t\u00e9ra, M., Brandolini, M., Napoli, A., Comte, B., & Pujos-Guillot, E. (2016). Feature selection methods for early predictive biomarker discovery using untargeted metabolomic data. Frontiers in Molecular Biosciences, 3, 30.","journal-title":"Frontiers in Molecular Biosciences"},{"key":"6181_CR25","first-page":"1157","volume":"3","author":"I Guyon","year":"2003","unstructured":"Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157\u20131182.","journal-title":"Journal of Machine Learning Research"},{"key":"6181_CR26","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1109\/34.58871","volume":"12","author":"LK Hansen","year":"1990","unstructured":"Hansen, L. K., & Salamon, P. (1990). Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12, 993\u20131001.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"4","key":"6181_CR27","doi-asserted-by":"crossref","DOI":"10.1002\/widm.1312","volume":"9","author":"A Holzinger","year":"2019","unstructured":"Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & M\u00fcller, H. (2019). Causability and explainability of artificial intelligence in medicine. WIREs Data Mining and Knowledge Discovery, 9(4), e1312.","journal-title":"WIREs Data Mining and Knowledge Discovery"},{"key":"6181_CR28","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.artmed.2016.03.003","volume":"69","author":"M Hoogendoorn","year":"2016","unstructured":"Hoogendoorn, M., Szolovits, P., Moons, L. M., & Numans, M. E. (2016). Utilizing uncoded consultation notes from electronic medical records for predictive modeling of colorectal cancer. Artificial Intelligence in Medicine, 69, 53\u201361.","journal-title":"Artificial Intelligence in Medicine"},{"issue":"1","key":"6181_CR29","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF01908075","volume":"2","author":"L Hubert","year":"1985","unstructured":"Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2(1), 193\u2013218.","journal-title":"Journal of Classification"},{"issue":"9","key":"6181_CR30","first-page":"1","volume":"5","author":"VA Huynh-Thu","year":"2010","unstructured":"Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., & Geurts, P. (2010). Inferring regulatory networks from expression data using tree-based methods. PLoS ONE, 5(9), 1\u201310.","journal-title":"PLoS ONE"},{"key":"6181_CR31","doi-asserted-by":"crossref","unstructured":"Jong, K., Mary, J., Cornu\u00e9jols, A., Marchiori, E., & Sebag, M. (2004). Ensemble feature ranking. In PKDD-LNCS, 2302 (pp. 267\u2013278).","DOI":"10.1007\/978-3-540-30116-5_26"},{"issue":"2","key":"6181_CR32","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/S0038-0717(99)00147-9","volume":"32","author":"C Kampichler","year":"2000","unstructured":"Kampichler, C., D\u017eeroski, S., & Wieland, R. (2000). Application of machine learning techniques to the analysis of soil ecological data bases: Relationships between habitat features and collembolan community characteristics. Soil Biology and Biochemistry, 32(2), 197\u2013209.","journal-title":"Soil Biology and Biochemistry"},{"issue":"2\u20133","key":"6181_CR33","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1023\/A:1007365207130","volume":"26","author":"A Karali\u010d","year":"1997","unstructured":"Karali\u010d, A., & Bratko, I. (1997). First order regression. Machine Learning, 26(2\u20133), 147\u2013176.","journal-title":"Machine Learning"},{"key":"6181_CR34","unstructured":"Katakis, I., Tsoumakas, G., & Vlahavas, I. (2008). Multilabel text classification for automated tag suggestion. In Proceedings of the ECML\/PKDD 2008 discovery challenge."},{"key":"6181_CR35","unstructured":"Kira, K. & Rendell, L.\u00a0A. (1992). The feature selection problem: Traditional methods and a new algorithm. In Proceedings of the tenth national conference on artificial intelligence, AAAI\u201992 (pp. 129\u2013134). AAAI Press."},{"key":"6181_CR36","doi-asserted-by":"crossref","unstructured":"Klimt, B. & Yang, Y. (2004). The enron corpus: A new dataset for email classification research. In ECML \u201904: Proceedings of the 18th European conference on machine learning\u2014LNCS 3201 (pp. 217\u2013226). Berlin: Springer.","DOI":"10.1007\/978-3-540-30115-8_22"},{"issue":"3","key":"6181_CR37","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1016\/j.patcog.2012.09.023","volume":"46","author":"D Kocev","year":"2013","unstructured":"Kocev, D., Vens, C., Struyf, J., & D\u017eeroski, S. (2013). Tree ensembles for predicting structured outputs. Pattern Recognition, 46(3), 817\u2013833.","journal-title":"Pattern Recognition"},{"key":"6181_CR38","first-page":"23","volume":"55","author":"I Kononenko","year":"2003","unstructured":"Kononenko, I., & Robnik-\u0160ikonja, M. (2003). Theoretical and empirical analysis of ReliefF and RReliefF. Machine Learning Journal, 55, 23\u201369.","journal-title":"Machine Learning Journal"},{"key":"6181_CR39","doi-asserted-by":"crossref","first-page":"e0144296","DOI":"10.1371\/journal.pone.0144296","volume":"10","author":"P Kralj Novak","year":"2015","unstructured":"Kralj Novak, P., Smailovi\u0107, J., Sluban, B., & Mozeti\u010d, I. (2015). Sentiment of emojis. PLoS ONE, 10, e0144296.","journal-title":"PLoS ONE"},{"key":"6181_CR40","unstructured":"Levati\u0107, J. (2017). Semi-supervised learning for structured output prediction. PhD thesis, Jo\u017eef Stefan Postgraduate School, Ljubljana, Slovenia."},{"issue":"14","key":"6181_CR41","doi-asserted-by":"crossref","first-page":"5691","DOI":"10.1021\/jm400328s","volume":"56","author":"J Levati\u0107","year":"2013","unstructured":"Levati\u0107, J., C\u00farak, J., Kralj, M., \u0160muc, T., Osmak, M., & Supek, F. (2013). Accurate models for p-gp drug recognition induced from a cancer cell line cytotoxicity screen. Journal of Medicinal Chemistry, 56(14), 5691\u20135708.","journal-title":"Journal of Medicinal Chemistry"},{"issue":"C","key":"6181_CR42","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.ins.2018.03.033","volume":"450","author":"J Levati\u0107","year":"2018","unstructured":"Levati\u0107, J., Kocev, D., Ceci, M., & D\u017eeroski, S. (2018). Semi-supervised trees for multi-target regression. Information Sciences, 450(C), 109\u2013127.","journal-title":"Information Sciences"},{"key":"6181_CR43","doi-asserted-by":"crossref","unstructured":"Li, G.-Z., You, M., Ge, L., Yang, J., & Yang, M. (2010). Feature selection for semi-supervised multi-label learning with application to gene function analysis. In Proceedings of the first ACM international conference on bioinformatics and computational biology (pp. 354\u2013357).","DOI":"10.1145\/1854776.1854828"},{"key":"6181_CR44","unstructured":"Lichman, M. (2013). UCI machine learning repository. http:\/\/archive.ics.uci.edu\/ml."},{"key":"6181_CR45","doi-asserted-by":"crossref","first-page":"1662","DOI":"10.1109\/TMM.2012.2199293","volume":"14","author":"Z Ma","year":"2012","unstructured":"Ma, Z., Nie, F., Yang, Y., Uijlings, J., Sebe, N., & Hauptmann, A. (2012). Discriminating joint feature analysis for multimedia data understanding. IEEE Transactions on Multimedia, 14, 1662\u20131672.","journal-title":"IEEE Transactions on Multimedia"},{"key":"6181_CR46","unstructured":"Moro, S., Cortez, P., & Laureano, R. (2011). Using data mining for bank direct marketing: An application of the crisp-dm methodology. In Proceedings of the European simulation and modelling conference."},{"key":"6181_CR47","first-page":"589","volume":"8","author":"R Nilsson","year":"2007","unstructured":"Nilsson, R., Pe\u00f1a, J. M., Bj\u00f6rkegren, J., & Tegn\u00e9r, J. (2007). Consistent feature selection for pattern recognition in polynomial time. Journal of Machine Learning Research, 8, 589\u2013612.","journal-title":"Journal of Machine Learning Research"},{"key":"6181_CR48","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825\u20132830.","journal-title":"Journal of Machine Learning Research"},{"key":"6181_CR49","doi-asserted-by":"crossref","unstructured":"Pestian, J.\u00a0P., Brew, C., Matykiewicz, P., Hovermale, D.\u00a0J., Johnson, N., Bretonnel\u00a0Cohen, K., & Duch, W. (2007). A shared task involving multi-label classification of clinical free text. In Proceedings of the workshop on BioNLP 2007: Biological, translational, and clinical language processing (BioNLP \u201907) (pp. 97\u2013104).","DOI":"10.3115\/1572392.1572411"},{"key":"6181_CR50","doi-asserted-by":"crossref","unstructured":"Petkovi\u0107, M., Ceci, M., Kersting, K., & D\u017eeroski, S. (2020). Estimating the importance of relational features by using gradient boosting. In D. Helic, G. Leitner, M. Stettinger, A. Felfernig, & Z. Ras (Eds.), International symposium on methodologies for intelligent systems (pp. 362\u2013371). Springer.","DOI":"10.1007\/978-3-030-59491-6_34"},{"key":"6181_CR51","doi-asserted-by":"crossref","unstructured":"Petkovi\u0107, M., D\u017eeroski, S., & Kocev, D. (2019). Ensemble-based feature ranking for semi-supervised classification. In P. Kralj Novak, T. \u0160muc, & S. D\u017eeroski (Eds.), Discovery science (pp. 290\u2013305). Springer.","DOI":"10.1007\/978-3-030-33778-0_23"},{"issue":"10","key":"6181_CR52","doi-asserted-by":"crossref","first-page":"129","DOI":"10.12700\/APH.17.10.2020.10.8","volume":"17","author":"M Petkovi\u0107","year":"2020","unstructured":"Petkovi\u0107, M., D\u017eeroski, S., & Kocev, D. (2020). Feature ranking for hierarchical multi-label classification with tree ensemble methods. Acta Polytechnica Hungarica, 17(10), 129\u2013148.","journal-title":"Acta Polytechnica Hungarica"},{"key":"6181_CR53","doi-asserted-by":"crossref","unstructured":"Petkovi\u0107, M., Kocev, D., & D\u017eeroski, S. (2018). Feature ranking with relief for multi-label classification: Does distance matter? In L. Soldatova, J. Vanschoren, G. Papadopoulos, & M. Ceci (Eds.), Discovery science (pp. 51\u201365). Springer.","DOI":"10.1007\/978-3-030-01771-2_4"},{"issue":"11","key":"6181_CR54","doi-asserted-by":"crossref","first-page":"2141","DOI":"10.1007\/s10994-020-05908-1","volume":"109","author":"M Petkovi\u0107","year":"2020","unstructured":"Petkovi\u0107, M., Kocev, D., & D\u017eeroski, S. (2020). Feature ranking for multi-target regression. Machine Learning, 109(11), 2141\u20132159.","journal-title":"Machine Learning"},{"key":"6181_CR55","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.neucom.2015.02.045","volume":"161","author":"O Reyes","year":"2015","unstructured":"Reyes, O., Morell, C., & Ventura, S. (2015). Scalable extensions of the reliefF algorithm for weighting and selecting features on the multi-label learning context. Neurocomputing, 161, 168\u2013182.","journal-title":"Neurocomputing"},{"issue":"19","key":"6181_CR56","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","volume":"23","author":"Y Saeys","year":"2007","unstructured":"Saeys, Y., Inza, I., & Larra\u00f1aga, P. (2007). A review of feature selection techniques in bioinformatics. Bioinformatics, 23(19), 2507\u20132517.","journal-title":"Bioinformatics"},{"issue":"C","key":"6181_CR57","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.patcog.2016.11.003","volume":"64","author":"R Sheikhpour","year":"2017","unstructured":"Sheikhpour, R., Sarram, M., Gharaghani, S., & Chahooki, M. (2017). A survey on semi-supervised feature selection methods. Pattern Recognition, 64(C), 141\u2013158.","journal-title":"Pattern Recognition"},{"issue":"1","key":"6181_CR58","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s10994-016-5546-z","volume":"104","author":"E Spyromitros-Xioufis","year":"2016","unstructured":"Spyromitros-Xioufis, E., Tsoumakas, G., Groves, W., & Vlahavas, I. (2016). Multi-target regression via input space expansion: Treating targets as inputs. Machine Learning, 104(1), 55\u201398.","journal-title":"Machine Learning"},{"key":"6181_CR59","doi-asserted-by":"crossref","unstructured":"Sta\u0144czyk, U., & Jain, L. C. (Eds.). (2015). Feature selection for data and pattern recognition. Studies in computational intelligence. Berlin: Springer.","DOI":"10.1007\/978-3-662-45620-0"},{"key":"6181_CR60","unstructured":"Stojanova, D. (2009). Estimating forest properties from remotely sensed data by using machine learning. M.Sc. Thesis. Jo\u017eef Stefan International Postgraduate School."},{"key":"6181_CR61","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1109\/tnnls.2020.3027314","volume":"4","author":"E Tjoa","year":"2020","unstructured":"Tjoa, E., & Guan, C. (2020). A survey on explainable artificial intelligence (XAI): Towards medical XAI. IEEE Transactions on Neural Networks and Learning Systems, 4, 5. https:\/\/doi.org\/10.1109\/tnnls.2020.3027314","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"6181_CR62","unstructured":"Trochidis, K., Tsoumakas, G., Kalliris, G., & Vlahavas, I. (2008). Multilabel classification of music into emotions. In 2008 International conference on music information retrieval (ISMIR 2008) (pp. 325\u2013330)."},{"issue":"1","key":"6181_CR63","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1186\/s12859-018-2023-7","volume":"19","author":"M Tsagris","year":"2018","unstructured":"Tsagris, M., Lagani, V., & Tsamardinos, I. (2018). Feature selection for high-dimensional temporal data. BMC Bioinformatics, 19(1), 17.","journal-title":"BMC Bioinformatics"},{"issue":"1\u20132","key":"6181_CR64","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1023\/B:MACH.0000035476.95130.99","volume":"57","author":"P Van Der Putten","year":"2004","unstructured":"Van Der Putten, P., & Van Someren, M. (2004). A bias-variance analysis of a real world learning problem: The coil challenge 2000. Machine Learning, 57(1\u20132), 177\u2013195.","journal-title":"Machine Learning"},{"issue":"2","key":"6181_CR65","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s10994-008-5077-3","volume":"73","author":"C Vens","year":"2008","unstructured":"Vens, C., Struyf, J., Schietgat, L., D\u017eeroski, S., & Blockeel, H. (2008). Decision trees for hierarchical multi-label classification. Machine Learning, 73(2), 185\u2013214.","journal-title":"Machine Learning"},{"key":"6181_CR66","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.imavis.2017.05.004","volume":"63","author":"X-D Wang","year":"2017","unstructured":"Wang, X.-D., Chen, R.-C., Qun Hong, C., Qiang Zeng, Z., & Li Zhou, Z. (2017). Semi-supervised multi-label feature selection via label correlation analysis with l1-norm graph embedding. Image and Vision Computing, 63, 10\u201323.","journal-title":"Image and Vision Computing"},{"issue":"3","key":"6181_CR67","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1109\/21.155943","volume":"22","author":"L Xu","year":"1992","unstructured":"Xu, L., Krzyzak, A., & Suen, C. Y. (1992). Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Transactions on Systems, Man, and Cybernetics, 22(3), 418\u2013435.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics"},{"issue":"7","key":"6181_CR68","doi-asserted-by":"crossref","first-page":"2013","DOI":"10.3390\/s18072013","volume":"18","author":"Y Zhou","year":"2018","unstructured":"Zhou, Y., Zhang, R., Wang, S., & Wang, F. (2018). Feature selection method based on high-resolution remote sensing images and the effect of sensitive features on classification accuracy. Sensors, 18(7), 2013.","journal-title":"Sensors"},{"key":"6181_CR69","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-031-01548-9","volume-title":"Introduction to semi-supervised learning","author":"X Zhu","year":"2009","unstructured":"Zhu, X., Goldberg, A. B., Brachman, R., & Dietterich, T. (2009). Introduction to semi-supervised learning. San Rafael: Morgan and Claypool Publishers."}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-022-06181-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-022-06181-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-022-06181-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T17:11:19Z","timestamp":1698253879000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-022-06181-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,22]]},"references-count":69,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["6181"],"URL":"https:\/\/doi.org\/10.1007\/s10994-022-06181-0","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"type":"print","value":"0885-6125"},{"type":"electronic","value":"1573-0565"}],"subject":[],"published":{"date-parts":[[2022,6,22]]},"assertion":[{"value":"11 March 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 January 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 June 2022","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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}