{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T04:03:18Z","timestamp":1748059398935,"version":"3.41.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T00:00:00Z","timestamp":1718928000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T00:00:00Z","timestamp":1718928000000},"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":["62376108"],"award-info":[{"award-number":["62376108"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-19637-3","type":"journal-article","created":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T07:02:01Z","timestamp":1718953321000},"page":"15553-15573","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Diversified deep hierarchical kernel ensemble regression"],"prefix":"10.1007","volume":"84","author":[{"given":"Zhifeng","family":"Liu","sequence":"first","affiliation":[]},{"given":"Zhengqin","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Stanley","family":"Ebhohimhen Abhadiomhen","sequence":"additional","affiliation":[]},{"given":"Xiaoqin","family":"Qian","sequence":"additional","affiliation":[]},{"given":"Xiang-Jun","family":"Shen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,21]]},"reference":[{"key":"19637_CR1","doi-asserted-by":"crossref","unstructured":"Boranda\u011f E, \u00d6z\u00e7ift A, Kaygusuz Y (2021) Development of majority vote ensemble feature selection algorithm augmentedwith rank allocation to enhance turkish text categorization. Turk J Electr Eng Comput Sci 29(2):514\u2013530","DOI":"10.3906\/elk-1911-116"},{"key":"19637_CR2","unstructured":"Yu J, Cai Z, He P, Xie G, Ling Q (2022) Multi-model ensemble learning method for human expression recognition. arXiv preprint arXiv:2203.14466"},{"key":"19637_CR3","unstructured":"Ahn E, Kumar A, Feng D, Fulham M, Kim J (2019) Unsupervised feature learning with k-means and an ensemble of deep convolutional neural networks for medical image classification. arXiv preprint arXiv:1906.03359"},{"key":"19637_CR4","doi-asserted-by":"crossref","unstructured":"Kazemi S, Minaei Bidgoli B, Shamshirband S, Karimi SM, Ghorbani MA, Chau K-W, Kazem Pour R (2018) Novel genetic-based negative correlation learning for estimating soil temperature. Eng Appl Comput Fluid Mech 12(1):506\u2013516","DOI":"10.1080\/19942060.2018.1463871"},{"key":"19637_CR5","doi-asserted-by":"crossref","unstructured":"Wu Y, Liu L, Xie Z, Chow K-H, Wei W (2021) Boosting ensemble accuracy by revisiting ensemble diversity metrics. In: Proc IEEE Int Conf Comput Vis Pattern Recognit pp 16469\u201316477","DOI":"10.1109\/CVPR46437.2021.01620"},{"issue":"5","key":"19637_CR6","doi-asserted-by":"publisher","first-page":"1651","DOI":"10.1214\/aos\/1024691352","volume":"26","author":"P Bartlett","year":"1998","unstructured":"Bartlett P, Freund Y, Lee WS, Schapire RE (1998) Boosting the margin: A new explanation for the effectiveness of voting methods. Ann Stat 26(5):1651\u20131686","journal-title":"Ann Stat"},{"issue":"2","key":"19637_CR7","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/BF00058655","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman L (1996) Bagging predictors. Mach Learn 24(2):123\u2013140","journal-title":"Mach Learn"},{"issue":"1","key":"19637_CR8","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"key":"19637_CR9","unstructured":"Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: Icml, vol 96, pp 148\u2013156. Citeseer"},{"key":"19637_CR10","doi-asserted-by":"crossref","unstructured":"Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189\u20131232","DOI":"10.1214\/aos\/1013203451"},{"key":"19637_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107583","volume":"109","author":"R Wang","year":"2021","unstructured":"Wang R, Kwong S, Wang X, Jia Y (2021) Active k-labelsets ensemble for multi-label classification. Pattern Recogn 109:107583","journal-title":"Pattern Recogn"},{"key":"19637_CR12","doi-asserted-by":"crossref","unstructured":"Wang B, Xue B, Zhang M (2020) Particle swarm optimisation for evolving deep neural networks for image classification by evolving and stacking transferable blocks. In: 2020 IEEE Congr Evol Comput (CEC) pp 1\u20138. IEEE","DOI":"10.1109\/CEC48606.2020.9185541"},{"key":"19637_CR13","doi-asserted-by":"crossref","unstructured":"Liu B, Gu L, Lu F (2019) Unsupervised ensemble strategy for retinal vessel segmentation. In: Int Conf Med Image Comput Comput Assist Interv, pp 111\u2013119. Springer","DOI":"10.1007\/978-3-030-32239-7_13"},{"key":"19637_CR14","doi-asserted-by":"crossref","unstructured":"Ali F, El-Sappagh S, Islam SR, Kwak D, Ali A, Imran M, Kwak K-S (2020) A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion. Inf Fusion 63:208\u2013222","DOI":"10.1016\/j.inffus.2020.06.008"},{"key":"19637_CR15","doi-asserted-by":"crossref","unstructured":"Zhang W, Jiang J, Shao Y, Cui B (2020) Snapshot boosting: a fast ensemble framework for deep neural networks. Sci China Inf Sci 63(1):1\u201312","DOI":"10.1007\/s11432-018-9944-x"},{"key":"19637_CR16","first-page":"16001","volume":"33","author":"S Zhang","year":"2020","unstructured":"Zhang S, Liu M, Yan J (2020) The diversified ensemble neural network. Adv Neural Inf Process Syst 33:16001\u201316011","journal-title":"Adv Neural Inf Process Syst"},{"key":"19637_CR17","unstructured":"Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Adv Neural Inf Process Syst 30"},{"issue":"5","key":"19637_CR18","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1007\/s10994-016-5618-0","volume":"106","author":"S Bhadra","year":"2017","unstructured":"Bhadra S, Kaski S, Rousu J (2017) Multi-view kernel completion. Mach Learn 106(5):713\u2013739","journal-title":"Mach Learn"},{"issue":"1","key":"19637_CR19","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/s13042-021-01394-6","volume":"13","author":"GA Khan","year":"2022","unstructured":"Khan GA, Hu J, Li T, Diallo B, Zhao Y (2022) Multi-view low rank sparse representation method for three-way clustering. Int J Mach Learn Cybern 13(1):233\u2013253","journal-title":"Int J Mach Learn Cybern"},{"key":"19637_CR20","unstructured":"Jacot A, Gabriel F, Hongler C (2018) Neural tangent kernel: Convergence and generalization in neural networks. Adv Neural Inf Process Syst 31"},{"key":"19637_CR21","doi-asserted-by":"crossref","unstructured":"Gretton A, Bousquet O, Smola A, Sch\u00f6lkopf B (2005) Measuring statistical dependence with hilbert-schmidt norms. In: Int Conf Algorithmic Learning Theory, pp 63\u201377. Springer","DOI":"10.1007\/11564089_7"},{"key":"19637_CR22","doi-asserted-by":"crossref","unstructured":"Mukkamala S, Sung AH, Abraham A (2003) Intrusion detection using ensemble of soft computing paradigms. In: Intell Syst Design Appl, pp 239\u2013248. Springer, ???","DOI":"10.1007\/978-3-540-44999-7_23"},{"key":"19637_CR23","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. In: Proceed 22nd Acm Sigkdd Int Conf Knowl Discov Data Min, pp 785\u2013794","DOI":"10.1145\/2939672.2939785"},{"key":"19637_CR24","unstructured":"Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T-Y (2017) Lightgbm: A highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst 30"},{"issue":"1","key":"19637_CR25","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1080\/01431160412331269698","volume":"26","author":"M Pal","year":"2005","unstructured":"Pal M (2005) Random forest classifier for remote sensing classification. Int J Remote Sens 26(1):217\u2013222","journal-title":"Int J Remote Sens"},{"issue":"6","key":"19637_CR26","doi-asserted-by":"publisher","first-page":"2131","DOI":"10.1109\/TCBB.2019.2911071","volume":"17","author":"A Ogunleye","year":"2019","unstructured":"Ogunleye A, Wang Q-G (2019) Xgboost model for chronic kidney disease diagnosis. IEEE\/ACM Trans Comput Biol Bioinf 17(6):2131\u20132140","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"19637_CR27","doi-asserted-by":"crossref","unstructured":"Shi Z, Zhang L, Liu Y, Cao X, Ye Y, Cheng M-M, Zheng G (2018) Crowd counting with deep negative correlation learning. In: Proceed IEEE Conf Comput Vis Pattern Recognit, pp 5382\u20135390","DOI":"10.1109\/CVPR.2018.00564"},{"key":"19637_CR28","doi-asserted-by":"crossref","unstructured":"Xue J, Wang Z, Kong D, Wang Y, Liu X, Fan W, Yuan S, Niu S, Li D (2021) Deep ensemble neural-like p systems for segmentation of central serous chorioretinopathy lesion. Inf Fusion 65:84\u201394","DOI":"10.1016\/j.inffus.2020.08.016"},{"key":"19637_CR29","unstructured":"Lin Z, Chen M, Ma Y (2010) The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. arXiv preprint arXiv:1009.5055"},{"key":"19637_CR30","doi-asserted-by":"crossref","unstructured":"Sch\u00f6lkopf B, Smola AJ, Bach F (2002) Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT press, ???","DOI":"10.7551\/mitpress\/4175.001.0001"},{"issue":"10","key":"19637_CR31","doi-asserted-by":"publisher","first-page":"2745","DOI":"10.1364\/JOSAA.15.002745","volume":"15","author":"MA Vorontsov","year":"1998","unstructured":"Vorontsov MA, Sivokon VP (1998) Stochastic parallel-gradient-descent technique for high-resolution wave-front phase-distortion correction. JOSA A 15(10):2745\u20132758","journal-title":"JOSA A"},{"key":"19637_CR32","doi-asserted-by":"crossref","unstructured":"Ma C, Qiu X, Beutel D, Lane N (2023) Gradient-less federated gradient boosting tree with learnable learning rates. In: Proceed 3rd Workshop Mach Learn Syst, pp 56\u201363","DOI":"10.1145\/3578356.3592579"},{"key":"19637_CR33","doi-asserted-by":"crossref","unstructured":"Lalev A, Alexandrova A (2023) Recurrent neural networks for forecasting social processes. In: 2023 Int Conf Big Data Knowl Control Syst Eng (BdKCSE), pp 1\u20135. IEEE","DOI":"10.1109\/BdKCSE59280.2023.10339767"},{"key":"19637_CR34","unstructured":"Wan A, Dunlap L, Ho D, Yin J, Lee S, Jin H, Petryk S, Bargal SA, Gonzalez JE (2020) Nbdt: neural-backed decision trees. arXiv preprint arXiv:2004.00221"},{"key":"19637_CR35","unstructured":"Luo ZT, Sang H, Mallick B (2022) Bamdt: Bayesian additive semi-multivariate decision trees for nonparametric regression. In: Int Conf Mach Learn, pp 14509\u201314526. PMLR"},{"issue":"1","key":"19637_CR36","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1093\/nsr\/nwy108","volume":"6","author":"Z-H Zhou","year":"2019","unstructured":"Zhou Z-H, Feng J (2019) Deep forest. National Science Review 6(1):74\u201386","journal-title":"Deep forest. National Science Review"},{"key":"19637_CR37","doi-asserted-by":"crossref","unstructured":"Fang C, Cheng L, Mao Y, Zhang D, Fang Y, Li G, Qi H, Jiao L (2023) Separating noisy samples from tail classes for long-tailed image classification with label noise. IEEE Trans Neural Netw Learn Syst","DOI":"10.1109\/TNNLS.2023.3291695"},{"key":"19637_CR38","first-page":"1","volume":"30","author":"V Fonti","year":"2017","unstructured":"Fonti V, Belitser E (2017) Feature selection using lasso. VU Amsterdam research paper in business analytics 30:1\u201325","journal-title":"VU Amsterdam research paper in business analytics"},{"key":"19637_CR39","doi-asserted-by":"crossref","unstructured":"Fang C, Wang Q, Cheng L, Gao Z, Pan C, Cao Z, Zheng Z, Zhang D (2023) Reliable mutual distillation for medical image segmentation under imperfect annotations. IEEE Trans Med Imaging","DOI":"10.1109\/TMI.2023.3237183"},{"key":"19637_CR40","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/j.neunet.2023.12.006","volume":"171","author":"J Yao","year":"2024","unstructured":"Yao J, Han L, Guo G, Zheng Z, Cong R, Huang X, Ding J, Yang K, Zhang D, Han J (2024) Position-based anchor optimization for point supervised dense nuclei detection. Neural Netw 171:159\u2013170","journal-title":"Neural Netw"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19637-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-19637-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19637-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T10:44:11Z","timestamp":1747997051000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-19637-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,21]]},"references-count":40,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["19637"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-19637-3","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2024,6,21]]},"assertion":[{"value":"26 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 April 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 June 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 June 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This work was funded in part by the National Natural Science Foundation of China (62376108).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}