{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T03:27:00Z","timestamp":1767065220976},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,1,28]],"date-time":"2024-01-28T00:00:00Z","timestamp":1706400000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,28]],"date-time":"2024-01-28T00:00:00Z","timestamp":1706400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"The Science and Technology Plan Project of Guizhou Province","award":["[2018] 5781","[2018] 5781","[2018] 5781","[2018] 5781","[2018] 5781"],"award-info":[{"award-number":["[2018] 5781","[2018] 5781","[2018] 5781","[2018] 5781","[2018] 5781"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s00530-023-01227-2","type":"journal-article","created":{"date-parts":[[2024,1,28]],"date-time":"2024-01-28T05:02:43Z","timestamp":1706418163000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["An ensemble pruning method considering classifiers\u2019 interaction based on information theory for facial expression recognition"],"prefix":"10.1007","volume":"30","author":[{"given":"Yiqing","family":"Wu","sequence":"first","affiliation":[]},{"given":"Danyang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xing","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yumei","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Shisong","family":"Huang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,28]]},"reference":[{"key":"1227_CR1","doi-asserted-by":"publisher","first-page":"5619","DOI":"10.1109\/TII.2022.3141400","volume":"18","author":"C Bisogni","year":"2022","unstructured":"Bisogni, C., Castiglione, A., Hossain, S., Narducci, F., Umer, S.: Impact of deep learning approaches on facial expression recognition in healthcare industries. IEEE Trans. Ind. Inf. 18, 5619\u20135627 (2022). https:\/\/doi.org\/10.1109\/TII.2022.3141400","journal-title":"IEEE Trans. Ind. Inf."},{"key":"1227_CR2","doi-asserted-by":"publisher","first-page":"15251","DOI":"10.1007\/s11042-017-5105-z","volume":"77","author":"D Li","year":"2018","unstructured":"Li, D., Wen, G.: MRMR-based ensemble pruning for facial expression recognition. Multimed. Tools Appl. 77, 15251\u201315272 (2018). https:\/\/doi.org\/10.1007\/s11042-017-5105-z","journal-title":"Multimed. Tools Appl."},{"key":"1227_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-020-02866-3","author":"VRR Chirra","year":"2021","unstructured":"Chirra, V.R.R., Uyyala, S.R., Kolli, V.K.K.: Virtual facial expression recognition using deep CNN with ensemble learning. J. Ambient Intell. Human. Comput. (2021). https:\/\/doi.org\/10.1007\/s12652-020-02866-3","journal-title":"J. Ambient Intell. Human. Comput."},{"key":"1227_CR4","doi-asserted-by":"publisher","first-page":"3749","DOI":"10.3390\/s22103749","volume":"22","author":"M Quiroz","year":"2022","unstructured":"Quiroz, M., Pati\u00f1o, R., Diaz-Amado, J., Cardinale, Y.: Group emotion detection based on social robot perception. Sensors 22, 3749 (2022). https:\/\/doi.org\/10.3390\/s22103749","journal-title":"Sensors"},{"key":"1227_CR5","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2022.784311","volume":"13","author":"Y Li","year":"2022","unstructured":"Li, Y., Zhong, Z., Zhang, F., Zhao, X.: Artificial intelligence-based human-computer interaction technology applied in consumer behavior analysis and experiential education. Front. Psychol. 13, 784311 (2022). https:\/\/doi.org\/10.3389\/fpsyg.2022.784311","journal-title":"Front. Psychol."},{"key":"1227_CR6","doi-asserted-by":"publisher","first-page":"1463","DOI":"10.1007\/s00530-023-01062-5","volume":"29","author":"S Huang","year":"2023","unstructured":"Huang, S., et al.: CSLSEP: an ensemble pruning algorithm based on clustering soft label and sorting for facial expression recognition. Multimed. Syst. 29, 1463\u20131479 (2023). https:\/\/doi.org\/10.1007\/s00530-023-01062-5","journal-title":"Multimed. Syst."},{"key":"1227_CR7","doi-asserted-by":"publisher","first-page":"3188","DOI":"10.1007\/s10489-019-01435-2","volume":"49","author":"D Li","year":"2019","unstructured":"Li, D., Wen, G., Li, X., Cai, X.: Graph-based dynamic ensemble pruning for facial expression recognition. Appl. Intell. 49, 3188\u20133206 (2019). https:\/\/doi.org\/10.1007\/s10489-019-01435-2","journal-title":"Appl. Intell."},{"key":"1227_CR8","doi-asserted-by":"publisher","first-page":"5473","DOI":"10.1007\/s11042-022-12321-4","volume":"82","author":"Z He","year":"2023","unstructured":"He, Z., et al.: Global and local fusion ensemble network for facial expression recognition. Multimed. Tools Appl. 82, 5473\u20135494 (2023). https:\/\/doi.org\/10.1007\/s11042-022-12321-4","journal-title":"Multimed. Tools Appl."},{"key":"1227_CR9","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1007\/s10115-018-1176-z","volume":"59","author":"D Li","year":"2019","unstructured":"Li, D., et al.: RTCRELIEF-F: an effective clustering and ordering-based ensemble pruning algorithm for facial expression recognition. Knowl. Inf. Syst. 59, 219\u2013250 (2019). https:\/\/doi.org\/10.1007\/s10115-018-1176-z","journal-title":"Knowl. Inf. Syst."},{"key":"1227_CR10","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12839","volume":"39","author":"C Pabba","year":"2022","unstructured":"Pabba, C., Kumar, P.: An intelligent system for monitoring students\u2019 engagement in large classroom teaching through facial expression recognition. Expert Syst. 39, e12839 (2022). https:\/\/doi.org\/10.1111\/exsy.12839","journal-title":"Expert Syst."},{"key":"1227_CR11","doi-asserted-by":"publisher","first-page":"4435","DOI":"10.1016\/j.aej.2021.09.066","volume":"61","author":"Y Nan","year":"2022","unstructured":"Nan, Y., Ju, J., Hua, Q., Zhang, H., Wang, B.: A-mobilenet: an approach of facial expression recognition. Alex. Eng. J. 61, 4435\u20134444 (2022). https:\/\/doi.org\/10.1016\/j.aej.2021.09.066","journal-title":"Alex. Eng. J."},{"key":"1227_CR12","doi-asserted-by":"publisher","first-page":"969","DOI":"10.1109\/TCDS.2020.3041642","volume":"13","author":"Y Zhou","year":"2020","unstructured":"Zhou, Y., Jin, L., Liu, H., Song, E.: Color facial expression recognition by quaternion convolutional neural network with Gabor attention. IEEE Trans. Cogn. Develop. Syst. 13, 969\u2013983 (2020). https:\/\/doi.org\/10.1109\/TCDS.2020.3041642","journal-title":"IEEE Trans. Cogn. Develop. Syst."},{"key":"1227_CR13","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-023-04572-x","author":"D Li","year":"2023","unstructured":"Li, D., Zhang, Z., Wen, G.: Classifier subset selection based on classifier representation and clustering ensemble. Appl. Intell. (2023). https:\/\/doi.org\/10.1007\/s10489-023-04572-x","journal-title":"Appl. Intell."},{"key":"1227_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105151","volume":"115","author":"MA Ganaie","year":"2022","unstructured":"Ganaie, M.A., Hu, M., Malik, A., Tanveer, M., Suganthan, P.: Ensemble deep learning: a review. Eng. Appl. Artif. Intell. 115, 105151 (2022). https:\/\/doi.org\/10.1016\/j.engappai.2022.105151","journal-title":"Eng. Appl. Artif. Intell."},{"key":"1227_CR15","doi-asserted-by":"publisher","first-page":"119766","DOI":"10.1109\/ACCESS.2021.3108838","volume":"9","author":"W Li","year":"2021","unstructured":"Li, W., Luo, M., Zhang, P., Huang, W.: A novel multi-feature joint learning ensemble framework for multi-label facial expression recognition. IEEE Access 9, 119766\u2013119777 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3108838","journal-title":"IEEE Access"},{"key":"1227_CR16","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.neucom.2021.11.045","volume":"482","author":"AM Mohammed","year":"2022","unstructured":"Mohammed, A.M., Onieva, E., Wo\u017aniak, M.: Selective ensemble of classifiers trained on selective samples. Neurocomputing 482, 197\u2013211 (2022). https:\/\/doi.org\/10.1016\/j.neucom.2021.11.045","journal-title":"Neurocomputing"},{"key":"1227_CR17","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/7560872","author":"R Hu","year":"2019","unstructured":"Hu, R., Zhou, S., Liu, Y., Tang, Z.: Margin-based pareto ensemble pruning: an ensemble pruning algorithm that learns to search optimized ensembles. Comput. Intell. Neurosci. (2019). https:\/\/doi.org\/10.1155\/2019\/7560872","journal-title":"Comput. Intell. Neurosci."},{"key":"1227_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patrec.2022.04.006","volume":"158","author":"S Fatemifar","year":"2022","unstructured":"Fatemifar, S., Asadi, S., Awais, M., Akbari, A., Kittler, J.: Face spoofing detection ensemble via multistage optimisation and pruning. Pattern Recogn. Lett. 158, 1\u20138 (2022). https:\/\/doi.org\/10.1016\/j.patrec.2022.04.006","journal-title":"Pattern Recogn. Lett."},{"key":"1227_CR19","doi-asserted-by":"publisher","first-page":"2568","DOI":"10.1007\/s10489-017-1106-x","volume":"48","author":"X Xia","year":"2018","unstructured":"Xia, X., Lin, T., Chen, Z.: Maximum relevancy maximum complementary based ordered aggregation for ensemble pruning. Appl. Intell. 48, 2568\u20132579 (2018). https:\/\/doi.org\/10.1007\/s10489-017-1106-x","journal-title":"Appl. Intell."},{"key":"1227_CR20","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.neucom.2017.06.052","volume":"275","author":"H Guo","year":"2018","unstructured":"Guo, H., et al.: Margin and diversity based ordering ensemble pruning. Neurocomputing 275, 237\u2013246 (2018). https:\/\/doi.org\/10.1016\/j.neucom.2017.06.052","journal-title":"Neurocomputing"},{"key":"1227_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.catena.2022.106055","volume":"212","author":"H Zhang","year":"2022","unstructured":"Zhang, H., Wu, S., Zhang, X., Han, L., Zhang, Z.: Slope stability prediction method based on the margin distance minimization selective ensemble. CATENA 212, 106055 (2022). https:\/\/doi.org\/10.1016\/j.catena.2022.106055","journal-title":"CATENA"},{"key":"1227_CR22","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.knosys.2017.03.031","volume":"125","author":"C-X Zhang","year":"2017","unstructured":"Zhang, C.-X., Zhang, J.-S., Yin, Q.-Y.: A ranking-based strategy to prune variable selection ensembles. Knowl. Based Syst. 125, 13\u201325 (2017). https:\/\/doi.org\/10.1016\/j.knosys.2017.03.031","journal-title":"Knowl. Based Syst."},{"key":"1227_CR23","doi-asserted-by":"publisher","first-page":"3766","DOI":"10.1109\/TNNLS.2019.2945116","volume":"31","author":"Y Bian","year":"2019","unstructured":"Bian, Y., Wang, Y., Yao, Y., Chen, H.: Ensemble pruning based on objection maximization with a general distributed framework. IEEE Trans. Neural Netw. Learn. Syst. 31, 3766\u20133774 (2019). https:\/\/doi.org\/10.1109\/TNNLS.2019.2945116","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"1227_CR24","doi-asserted-by":"publisher","first-page":"8299","DOI":"10.3233\/JIFS-189149","volume":"39","author":"Z Ni","year":"2020","unstructured":"Ni, Z., Xia, P., Zhu, X., Ding, Y., Ni, L.: A novel ensemble pruning approach based on information exchange glowworm swarm optimization and complementarity measure. J. Intell. Fuzzy Syst. 39, 8299\u20138313 (2020). https:\/\/doi.org\/10.3233\/JIFS-189149","journal-title":"J. Intell. Fuzzy Syst."},{"key":"1227_CR25","doi-asserted-by":"publisher","first-page":"8785","DOI":"10.1007\/s00500-021-05800-7","volume":"25","author":"X Gu","year":"2021","unstructured":"Gu, X., Guo, J.: A feature subset selection algorithm based on equal interval division and three-way interaction information. Soft. Comput. 25, 8785\u20138795 (2021). https:\/\/doi.org\/10.1007\/s00500-021-05800-7","journal-title":"Soft. Comput."},{"key":"1227_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115365","volume":"183","author":"L Wang","year":"2021","unstructured":"Wang, L., Jiang, S., Jiang, S.: A feature selection method via analysis of relevance, redundancy, and interaction. Expert Syst. Appl. 183, 115365 (2021). https:\/\/doi.org\/10.1016\/j.eswa.2021.115365","journal-title":"Expert Syst. Appl."},{"key":"1227_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108603","volume":"127","author":"J Wan","year":"2022","unstructured":"Wan, J., et al.: R2CI: information theoretic-guided feature selection with multiple correlations. Pattern Recogn. 127, 108603 (2022). https:\/\/doi.org\/10.1016\/j.patcog.2022.108603","journal-title":"Pattern Recogn."},{"key":"1227_CR28","doi-asserted-by":"publisher","first-page":"10419","DOI":"10.1007\/s13369-022-06590-2","volume":"47","author":"Z Li","year":"2022","unstructured":"Li, Z.: A feature selection method using dynamic dependency and redundancy analysis. Arab. J. Sci. Eng. 47, 10419\u201310433 (2022). https:\/\/doi.org\/10.1007\/s13369-022-06590-2","journal-title":"Arab. J. Sci. Eng."},{"key":"1227_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2019.04.046","volume":"494","author":"G Sosa-Cabrera","year":"2019","unstructured":"Sosa-Cabrera, G., Garcia-Torres, M., Gomez-Guerrero, S., Schaerer, C.E., Divina, F.: A multivariate approach to the symmetrical uncertainty measure: application to feature selection problem. Inf. Sci. 494, 1\u201320 (2019). https:\/\/doi.org\/10.1016\/j.ins.2019.04.046","journal-title":"Inf. Sci."},{"key":"1227_CR30","doi-asserted-by":"publisher","first-page":"5772","DOI":"10.3390\/jcm11195772","volume":"11","author":"X Jiang","year":"2022","unstructured":"Jiang, X., Xu, C.: Deep learning and machine learning with grid search to predict later occurrence of breast cancer metastasis using clinical data. J. Clin. Med. 11, 5772 (2022). https:\/\/doi.org\/10.3390\/jcm11195772","journal-title":"J. Clin. Med."},{"key":"1227_CR31","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2023.01.021","volume":"146","author":"Q Li","year":"2022","unstructured":"Li, Q.: Functional connectivity inference from FMRI data using multivariate information measures. Neural Netw. 146, 85\u201397 (2022). https:\/\/doi.org\/10.1016\/j.neunet.2023.01.021","journal-title":"Neural Netw."},{"key":"1227_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107701","volume":"64","author":"IJ Goodfellow","year":"2015","unstructured":"Goodfellow IJ, Erhan D, Carrier PL et al., Challenges in representation learning: A report on three machine learning contests, Neural Networks, 64 (2015) 59\u201363.\u00a0https:\/\/doi.org\/10.1016\/j.neunet.2014.09.005","journal-title":"Neural Networks"},{"key":"1227_CR33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2010.5543262","author":"Lucey P","year":"2010","unstructured":"Lucey P, Cohn JF, Kanade T et al., The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression, IEEE Computer Society Conference on Computer Vision & Pattern Recognition Workshops, 2010. https:\/\/doi.org\/10.1109\/CVPRW.2010.5543262","journal-title":"IEEE Computer Society Conference on Computer Vision & Pattern Recognition Workshops"},{"key":"1227_CR34","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2020.3028075","author":"Michael Lyons","year":"2020","unstructured":"Michael J Lyons, Miyuki Kamachi, & Jiro Gyoba (2020) Coding Facial Ex-pressions with Gabor Wavelets (IVC Special Issue). https:\/\/doi.org\/10.5281\/zenodo.4029680","journal-title":"IVC Special Issue"},{"key":"1227_CR35","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.5147170","author":"Lyons","year":"2021","unstructured":"Lyons, Michael J (2021) \"Excavating AI\" Re-excavated: Debunking a Fallacious Account of the JAFFE Dataset. Zenodo. https:\/\/doi.org\/10.5281\/zenodo.5147170","journal-title":"Zenodo"},{"key":"1227_CR36","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.1080\/02699930701626582","volume":"22","author":"Ellen Goeleven","year":"2008","unstructured":"Goeleven E, Raedt RD, Leyman L, Verschuere B (2008) The karolinska directed emotional faces: A validation study. Cogn Emot 22:1094\u20131118. https:\/\/doi.org\/10.1080\/02699930701626582","journal-title":"Cogn Emot"},{"key":"1227_CR37","doi-asserted-by":"publisher","first-page":"1377","DOI":"10.1080\/02699930903485076","volume":"24","author":"Oliver Langner","year":"2010","unstructured":"Oliver Langner, Ron Dotsch, Gijsbert Bijlstra, Daniel H. J. Wigboldus, Skyler T. Hawk & Ad van Knippenberg (2010) Presentation and validation of the Radboud Faces Database, Cognition and Emotion, 24:8, 1377-1388, https:\/\/doi.org\/10.1080\/02699930903485076","journal-title":"Cognition and Emotion"},{"key":"1227_CR38","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1007\/s10994-010-5172-0","volume":"81","author":"I Partalas","year":"2010","unstructured":"Partalas, I., Tsoumakas, G., Vlahavas, I.: An ensemble uncertainty aware measure for directed hill climbing ensemble pruning. Mach. Learn. 81, 257\u2013282 (2010). https:\/\/doi.org\/10.1007\/s10994-010-5172-0","journal-title":"Mach. Learn."},{"key":"1227_CR39","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1007\/978-3-642-33460-3_27","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"N Li","year":"2012","unstructured":"Li, N., Yu, Y., Zhou, Z.-H., Flach, P.A., De Bie, T., Cristianini, N.: Diversity regularized ensemble pruning. In: Flach, P.A., De Bie, T., Cristianini, N. (eds.) Machine Learning and Knowledge Discovery in Databases, pp. 330\u2013345. Springer, Berlin (2012). https:\/\/doi.org\/10.1007\/978-3-642-33460-3_27"},{"key":"1227_CR40","doi-asserted-by":"publisher","first-page":"816","DOI":"10.1007\/s10489-015-0729-z","volume":"44","author":"Q Dai","year":"2016","unstructured":"Dai, Q., Han, X.: An efficient ordering-based ensemble pruning algorithm via dynamic programming. Appl. Intell. 44, 816\u2013830 (2016). https:\/\/doi.org\/10.1007\/s10489-015-0729-z","journal-title":"Appl. Intell."},{"key":"1227_CR41","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2011.234","author":"LI Kuncheva","year":"2013","unstructured":"Kuncheva, L.I.: A bound on kappa-error diagrams for analysis of classifier ensembles. IEEE Educ. Activities Depart. (2013). https:\/\/doi.org\/10.1109\/TKDE.2011.234","journal-title":"IEEE Educ. Activities Depart."},{"key":"1227_CR42","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1016\/j.neucom.2013.06.026","volume":"122","author":"Q Dai","year":"2013","unstructured":"Dai, Q.: A novel ensemble pruning algorithm based on randomized greedy selective strategy and ballot. Neurocomputing 122, 258\u2013265 (2013). https:\/\/doi.org\/10.1016\/j.neucom.2013.06.026","journal-title":"Neurocomputing"},{"key":"1227_CR43","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.asoc.2017.04.058","volume":"58","author":"Q Dai","year":"2017","unstructured":"Dai, Q., Ye, R., Liu, Z.: Considering diversity and accuracy simultaneously for ensemble pruning. Appl. Soft Comput. 58, 75\u201391 (2017). https:\/\/doi.org\/10.1016\/j.asoc.2017.04.058","journal-title":"Appl. Soft Comput."},{"key":"1227_CR44","doi-asserted-by":"publisher","first-page":"1185","DOI":"10.1587\/transinf.2021EDP7213","volume":"105","author":"S Madhusudhanan","year":"2022","unstructured":"Madhusudhanan, S., Jaganathan, S.: Data augmented incremental learning (DAIL) for unsupervised data. IEICE Trans. Inf. Syst. 105, 1185\u20131195 (2022). https:\/\/doi.org\/10.1587\/transinf.2021EDP7213","journal-title":"IEICE Trans. Inf. Syst."},{"key":"1227_CR45","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1109\/34.588027","volume":"19","author":"K Woods","year":"1997","unstructured":"Woods, K., Kegelmeyer, W., Bowyer, K.: Combination of multiple classifiers using local accuracy estimates. IEEE Trans. Pattern Anal. Mach. Intell. 19, 405\u2013410 (1997). https:\/\/doi.org\/10.1109\/34.588027","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1227_CR46","doi-asserted-by":"publisher","unstructured":"Giacinto, G., Roli, F.: Dynamic classifier selection based on multiple classifier behaviour. Pattern Recogn. 34, 1879\u20131881 (2001). https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0031320300001503. https:\/\/doi.org\/10.1016\/S0031-3203(00)00150-3","DOI":"10.1016\/S0031-3203(00)00150-3"},{"key":"1227_CR47","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1016\/j.neucom.2014.07.063","volume":"150","author":"F Markatopoulou","year":"2015","unstructured":"Markatopoulou, F., Tsoumakas, G., Vlahavas, I.: Dynamic ensemble pruning based on multi-label classification. Neurocomputing 150, 501\u2013512 (2015). https:\/\/doi.org\/10.1016\/j.neucom.2014.07.063","journal-title":"Neurocomputing"},{"key":"1227_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.dam.2022.04.014","volume":"318","author":"Z Yang","year":"2022","unstructured":"Yang, Z., Lu, H., Yu, Q.: Critical independent sets of K\u00f6nig\u2013Egerv\u00e1ry graphs. Discrete Appl. Math. 318, 1\u20135 (2022). https:\/\/doi.org\/10.1016\/j.dam.2022.04.014","journal-title":"Discrete Appl. Math."},{"key":"1227_CR49","unstructured":"Dua, D., Graff, C.: UCI machine learning repository (2017). http:\/\/archive.ics.uci.edu\/ml"},{"key":"1227_CR50","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/s11222-009-9153-8","volume":"21","author":"T Fushiki","year":"2011","unstructured":"Fushiki, T.: Estimation of prediction error by using $$k$$-fold cross-validation. Stat. Comput. 21, 137\u2013146 (2011). https:\/\/doi.org\/10.1007\/s11222-009-9153-8","journal-title":"Stat. Comput."},{"key":"1227_CR51","doi-asserted-by":"publisher","first-page":"2825","DOI":"10.48550\/arXiv.1201.0490","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011). https:\/\/doi.org\/10.48550\/arXiv.1201.0490","journal-title":"J. Mach. Learn. Res."}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-023-01227-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-023-01227-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-023-01227-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,14]],"date-time":"2024-02-14T06:22:18Z","timestamp":1707891738000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-023-01227-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,28]]},"references-count":51,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["1227"],"URL":"https:\/\/doi.org\/10.1007\/s00530-023-01227-2","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,28]]},"assertion":[{"value":"19 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"46"}}