{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:11:11Z","timestamp":1757617871556,"version":"3.44.0"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T00:00:00Z","timestamp":1745884800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T00:00:00Z","timestamp":1745884800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62262034","62262035"],"award-info":[{"award-number":["62262034","62262035"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s13042-025-02655-4","type":"journal-article","created":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T16:33:53Z","timestamp":1745944433000},"page":"6957-6976","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Tree comprehensive diversity-based pruned deep forest"],"prefix":"10.1007","volume":"16","author":[{"given":"Jiaman","family":"Ding","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangyu","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lianyin","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaodong","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,29]]},"reference":[{"issue":"6","key":"2655_CR1","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390. https:\/\/doi.org\/10.1145\/3065386","journal-title":"Commun ACM"},{"key":"2655_CR2","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770\u2013778. IEEE, Las Vegas. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"issue":"6","key":"2655_CR3","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","volume":"29","author":"G Hinton","year":"2012","unstructured":"Hinton G, Deng L, Yu D, Dahl GE, Mohamed A-R, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath TN, Kingsbury B (2012) Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag 29(6):82\u201397. https:\/\/doi.org\/10.1109\/MSP.2012.2205597","journal-title":"IEEE Signal Process Mag"},{"key":"2655_CR4","unstructured":"Kenton JDM-WC, Toutanova, LK (2019) BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT, pp. 4171\u20134186"},{"key":"2655_CR5","first-page":"507","volume":"35","author":"L Grinsztajn","year":"2022","unstructured":"Grinsztajn L, Oyallon E, Varoquaux G (2022) Why do tree-based models still outperform deep learning on typical tabular data? Adv Neural Inf Process Syst 35:507\u2013520","journal-title":"Adv Neural Inf Process Syst"},{"key":"2655_CR6","first-page":"3553","volume-title":"IJCAI","author":"Z-H Zhou","year":"2017","unstructured":"Zhou Z-H, Feng J (2017) Deep forest: towards an alternative to deep neural networks. IJCAI. IEEE, New York, pp 3553\u20133559"},{"key":"2655_CR7","doi-asserted-by":"publisher","DOI":"10.1145\/3342241","author":"Y-L Zhang","year":"2019","unstructured":"Zhang Y-L, Zhou J, Zheng W, Feng J, Li L, Liu Z, Li M, Zhang Z, Chen C, Li X, Qi YA, Zhou Z-H (2019) Distributed deep forest and its application to automatic detection of cash-out fraud. ACM Trans Intell Syst Technol. https:\/\/doi.org\/10.1145\/3342241","journal-title":"ACM Trans Intell Syst Technol"},{"issue":"10","key":"2655_CR8","doi-asserted-by":"publisher","first-page":"2798","DOI":"10.1109\/JBHI.2020.3019505","volume":"24","author":"L Sun","year":"2020","unstructured":"Sun L, Mo Z, Yan F, Xia L, Shan F, Ding Z, Song B, Gao W, Shao W, Shi F, Yuan H, Jiang H, Wu D, Wei Y, Gao Y, Sui H, Zhang D, Shen D (2020) Adaptive feature selection guided deep forest for COVID-19 classification with chest CT. IEEE J Biomed Health Inf 24(10):2798\u20132805. https:\/\/doi.org\/10.1109\/JBHI.2020.3019505","journal-title":"IEEE J Biomed Health Inf"},{"key":"2655_CR9","doi-asserted-by":"publisher","first-page":"104557","DOI":"10.1016\/j.cageo.2020.104557","volume":"144","author":"L Weng","year":"2020","unstructured":"Weng L, Qian M, Xia M, Xu Y, Li C (2020) Land use\/land cover recognition in arid zone using a multi-dimensional multi-grained residual Forest. Comput Geosci 144:104557. https:\/\/doi.org\/10.1016\/j.cageo.2020.104557","journal-title":"Comput Geosci"},{"key":"2655_CR10","doi-asserted-by":"publisher","unstructured":"Wu T, Zhao Y, Liu L, Li H, Xu W, Chen C (2018) A novel hierarchical regression approach for human facial age estimation based on deep forest. In: 2018 IEEE 15th international conference on networking, sensing and control (ICNSC), 1\u20136. IEEE, Zhuhai. https:\/\/doi.org\/10.1109\/ICNSC.2018.8361338","DOI":"10.1109\/ICNSC.2018.8361338"},{"issue":"1","key":"2655_CR11","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1109\/TETCI.2020.3034900","volume":"6","author":"Y Ma","year":"2022","unstructured":"Ma Y, Lei Y, Wang T (2022) A natural scene recognition learning based on label correlation. IEEE Trans Emerg Top Comput Intell 6(1):150\u2013158. https:\/\/doi.org\/10.1109\/TETCI.2020.3034900","journal-title":"IEEE Trans Emerg Top Comput Intell"},{"key":"2655_CR12","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forest. Mach Learn 45:5\u201332","journal-title":"Mach Learn"},{"issue":"1","key":"2655_CR13","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","volume":"63","author":"P Geurts","year":"2006","unstructured":"Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Machine Learning 63(1):3\u201342. https:\/\/doi.org\/10.1007\/s10994-006-6226-1","journal-title":"Machine Learning"},{"issue":"6","key":"2655_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3532193","volume":"16","author":"P Ma","year":"2022","unstructured":"Ma P, Wu Y, Li Y, Guo L, Jiang H, Zhu X, Wu X (2022) HW-forest: deep forest with hashing screening and window screening. ACM Trans Knowl Discov Data 16(6):1\u201324. https:\/\/doi.org\/10.1145\/3532193","journal-title":"ACM Trans Knowl Discov Data"},{"key":"2655_CR15","doi-asserted-by":"crossref","unstructured":"Fan Y, Qi L, Tie Y (2019) The cascade improved model based deep forest for small-scale datasets classification. In: 2019 8th international symposium on next generation electronics (ISNE). IEEE, New York, pp. 1\u20133","DOI":"10.1109\/ISNE.2019.8896445"},{"key":"2655_CR16","doi-asserted-by":"publisher","unstructured":"Pang M, Ting K-M, Zhao P, Zhou Z-H (2018) Improving deep forest by confidence screening. In: 2018 IEEE international conference on data mining (ICDM), pp. 1194\u20131199. IEEE, Singapore. https:\/\/doi.org\/10.1109\/ICDM.2018.00158","DOI":"10.1109\/ICDM.2018.00158"},{"key":"2655_CR17","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.neucom.2021.12.075","volume":"475","author":"P Ma","year":"2022","unstructured":"Ma P, Wu Y, Li Y, Guo L, Li Z (2022) DBC-forest: deep forest with binning confidence screening. Neurocomputing 475:112\u2013122. https:\/\/doi.org\/10.1016\/j.neucom.2021.12.075","journal-title":"Neurocomputing"},{"key":"2655_CR18","doi-asserted-by":"publisher","unstructured":"Yi-He C, Shen-Huan L, Yuan J(2021) Improving deep forest by exploiting high-order interactions. In: Bailey, J., Miettinen, P., Koh, Y.S., Tao, D., Wu, X. (eds.) 2021 IEEE international conference on data mining (ICDM), pp. 1030\u20131035. IEEE, Auckland. https:\/\/doi.org\/10.1109\/ICDM51629.2021.00118","DOI":"10.1109\/ICDM51629.2021.00118"},{"issue":"1","key":"2655_CR19","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1093\/bib\/bbz152","volume":"22","author":"Y Chu","year":"2021","unstructured":"Chu Y, Kaushik AC, Wang X, Wang W, Zhang Y, Shan X, Salahub DR, Xiong Y, Wei D-Q (2021) DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features. Brief Bioinf 22(1):451\u2013462. https:\/\/doi.org\/10.1093\/bib\/bbz152","journal-title":"Brief Bioinf"},{"issue":"5","key":"2655_CR20","doi-asserted-by":"publisher","first-page":"2049","DOI":"10.1007\/s12206-019-0408-9","volume":"33","author":"X Liu","year":"2019","unstructured":"Liu X, Tian Y, Lei X, Liu M, Wen X, Huang H, Wang H (2019) Deep forest based intelligent fault diagnosis of hydraulic turbine. J Mech Sci Technol 33(5):2049\u20132058. https:\/\/doi.org\/10.1007\/s12206-019-0408-9","journal-title":"J Mech Sci Technol"},{"key":"2655_CR21","doi-asserted-by":"publisher","first-page":"107499","DOI":"10.1016\/j.patcog.2020.107499","volume":"107","author":"C Ma","year":"2020","unstructured":"Ma C, Liu Z, Cao Z, Song W, Zhang J, Zeng W (2020) Cost-sensitive deep forest for price prediction. Pattern Recognit 107:107499. https:\/\/doi.org\/10.1016\/j.patcog.2020.107499","journal-title":"Pattern Recognit"},{"key":"2655_CR22","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 (2017) Considering diversity and accuracy simultaneously for ensemble pruning. Appl Soft Comput 58:75\u201391. https:\/\/doi.org\/10.1016\/j.asoc.2017.04.058","journal-title":"Appl Soft Comput"},{"key":"2655_CR23","doi-asserted-by":"publisher","unstructured":"Caruana R, Niculescu-Mizil A, Crew G, Ksikes A (2004) Ensemble selection from libraries of models. In: Twenty-first international conference on machine learning - ICML \u201904, p. 18. ACM Press, Banff, Alberta, Canada. https:\/\/doi.org\/10.1145\/1015330.1015432","DOI":"10.1145\/1015330.1015432"},{"key":"2655_CR24","first-page":"1315","volume":"7","author":"Y Zhang","year":"2006","unstructured":"Zhang Y, Burer S, Street WN (2006) Ensemble pruning via semi-definite programming. J Mach Learn Res 7:1315\u20131338","journal-title":"J Mach Learn Res"},{"key":"2655_CR25","unstructured":"Margineantu, DD, Dietterich TG (1997) Pruning adaptive boosting. In: Proceedings of the fourteenth international conference on machine learning. ICML \u201997, pp. 211\u2013218. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA"},{"key":"2655_CR26","doi-asserted-by":"publisher","unstructured":"Lu Z, Wu X, Zhu X, Bongard J (2010) Ensemble pruning via individual contribution ordering. In: Rao, B., Krishnapuram, B., Tomkins, A., Yang, Q. (eds.) Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, 871\u2013880. ACM, Washington DC USA. https:\/\/doi.org\/10.1145\/1835804.1835914","DOI":"10.1145\/1835804.1835914"},{"issue":"6","key":"2655_CR27","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1016\/j.patrec.2013.01.003","volume":"34","author":"L Guo","year":"2013","unstructured":"Guo L, Boukir S (2013) Margin-based ordered aggregation for ensemble pruning. Pattern Recognit Lett 34(6):603\u2013609. https:\/\/doi.org\/10.1016\/j.patrec.2013.01.003","journal-title":"Pattern Recognit Lett"},{"issue":"2","key":"2655_CR28","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/S0893-6080(02)00187-9","volume":"16","author":"B Bakker","year":"2003","unstructured":"Bakker B, Heskes T (2003) Clustering ensembles of neural network models. Neural Netw 16(2):261\u2013269. https:\/\/doi.org\/10.1016\/S0893-6080(02)00187-9","journal-title":"Neural Netw"},{"key":"2655_CR29","doi-asserted-by":"publisher","unstructured":"Lazarevic A, Obradovic Z (2001) Effective pruning of neural network classifier ensembles. In: IJCNN\u201901. International joint conference on neural networks. proceedings (Cat. No.01CH37222), vol. 2, pp. 796\u2013801. IEEE, Washington, DC, USA. https:\/\/doi.org\/10.1109\/IJCNN.2001.939461","DOI":"10.1109\/IJCNN.2001.939461"},{"key":"2655_CR30","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.patrec.2016.01.029","volume":"74","author":"GDC Cavalcanti","year":"2016","unstructured":"Cavalcanti GDC, Oliveira LS, Moura TJM, Carvalho GV (2016) Combining diversity measures for ensemble pruning. Pattern Recognit Lett 74:38\u201345. https:\/\/doi.org\/10.1016\/j.patrec.2016.01.029","journal-title":"Pattern Recognit Lett"},{"key":"2655_CR31","doi-asserted-by":"crossref","unstructured":"Qian C, Yu Y, Zhou Z-H (2015) Pareto ensemble pruning. In: Proceedings of the twenty-ninth AAAI conference on artificial intelligence. AAAI\u201915, pp. 2935\u20132941. AAAI Press, Austin, Texas","DOI":"10.1609\/aaai.v29i1.9579"},{"key":"2655_CR32","first-page":"29","volume":"2","author":"Shelby J Haberman","year":"2010","unstructured":"Haberman Shelby J (2010) Limits on the accuracy of linking. ETS Res Rep 2:29","journal-title":"ETS Res Rep"},{"key":"2655_CR33","doi-asserted-by":"publisher","DOI":"10.1201\/b12207","volume-title":"Ensemble methods: foundations and algorithms","author":"ZH Zhou","year":"2012","unstructured":"Zhou ZH (2012) Ensemble methods: foundations and algorithms. CRC Press, Boca Raton"},{"key":"2655_CR34","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 (2012) Diversity regularized ensemble pruning. In: Hutchison D, Kanade T, Kittler J, Kleinberg JM, Mattern F, Mitchell JC, Naor M, Nierstrasz O, Pandu Rangan C, Steffen B, Sudan M, Terzopoulos D, Tygar D, Vardi MY, Weikum G, Flach PA, De Bie T, Cristianini N (eds) Machine learning and knowledge discovery in databases. Springer, Berlin, pp 330\u2013345"},{"issue":"1","key":"2655_CR35","first-page":"22","volume":"66","author":"G Yule Udny","year":"1899","unstructured":"Yule Udny G (1899) On the association of attributes in statistics, with examples from the material of the childhood society, & c. Proceed Roy Soc Lon 66(1):22\u201323","journal-title":"Proceed Roy Soc Lon"},{"issue":"1","key":"2655_CR36","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1177\/001316446002000104","volume":"20","author":"J Cohen","year":"1960","unstructured":"Cohen J (1960) A coefficient of agreement for nominal scales. Edu Psychol Measur 20(1):37\u201346","journal-title":"Edu Psychol Measur"},{"key":"2655_CR37","unstructured":"Skalak DB (1996) The sources of increased accuracy for two proposed boosting algorithms. In: AAAI conference on artificial intelligence"},{"key":"2655_CR38","volume-title":"Bias plus variance decomposition for zero-one loss functions","author":"R Kohavi","year":"1997","unstructured":"Kohavi R, Wolpert DH (1997) Bias plus variance decomposition for zero-one loss functions. Morgan Kaufmann Publishers Inc, Burlington"},{"key":"2655_CR39","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45164-1_12","volume-title":"Diversity versus quality in classification ensembles based on feature selection","author":"P Cunningham","year":"2000","unstructured":"Cunningham P, Carney J (2000) Diversity versus quality in classification ensembles based on feature selection. Springer-Verlag, Cham"},{"issue":"3","key":"2655_CR40","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1007\/s11704-018-7151-8","volume":"12","author":"T Sun","year":"2018","unstructured":"Sun T, Zhou Z-H (2018) Structural diversity for decision tree ensemble learning. Front Comput Sci 12(3):560\u2013570. https:\/\/doi.org\/10.1007\/s11704-018-7151-8","journal-title":"Front Comput Sci"},{"key":"2655_CR41","doi-asserted-by":"publisher","unstructured":"Broder AZ (1998) On the resemblance and containment of documents. In: Proceedings. Compression and complexity of SEQUENCES 1997 (Cat. No.97TB100171), 21\u201329. IEEE Comput. Soc, Salerno, Italy. https:\/\/doi.org\/10.1109\/SEQUEN.1997.666900","DOI":"10.1109\/SEQUEN.1997.666900"},{"key":"2655_CR42","volume-title":"UCI Machine Learning Repository","author":"D Dua","year":"2017","unstructured":"Dua D, Graff C (2017) UCI Machine Learning Repository. University of California, Irvine"},{"key":"2655_CR43","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1013203451","author":"JH Friedman","year":"2001","unstructured":"Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat. https:\/\/doi.org\/10.1214\/aos\/1013203451","journal-title":"Ann Stat"},{"key":"2655_CR44","doi-asserted-by":"publisher","unstructured":"Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. In: Krishnapuram, B., Shah, M., Smola, A.J., Aggarwal, C.C., Shen, D., Rastogi, R. (eds.) Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 785\u2013794. ACM, San Francisco California USA. https:\/\/doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"key":"2655_CR45","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1\u201330","journal-title":"J Mach Learn Res"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02655-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02655-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02655-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T11:00:47Z","timestamp":1757156447000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02655-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,29]]},"references-count":45,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["2655"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02655-4","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2025,4,29]]},"assertion":[{"value":"26 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 April 2025","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 relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}