{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T02:06:32Z","timestamp":1762999592767,"version":"3.37.3"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2020,8,2]],"date-time":"2020-08-02T00:00:00Z","timestamp":1596326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,8,2]],"date-time":"2020-08-02T00:00:00Z","timestamp":1596326400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2020,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The features in some machine learning datasets can naturally be divided into groups. This is the case with genomic data, where features can be grouped by chromosome. In many applications it is common for these groupings to be ignored, as interactions may exist between features belonging to different groups. However, including a group that does not influence a response introduces noise when fitting a model, leading to suboptimal predictive accuracy. Here we present two general frameworks for the generation and combination of meta-features when feature groupings are present. Furthermore, we make comparisons to multi-target learning, given that one is typically interested in predicting multiple phenotypes. We evaluated the frameworks and multi-target learning approaches on a genomic rice dataset where the regression task is to predict plant phenotype. Our results demonstrate that there are use cases for both the meta and multi-target approaches, given that overall, they significantly outperform the base case.<\/jats:p>","DOI":"10.1007\/s10994-020-05881-9","type":"journal-article","created":{"date-parts":[[2020,8,2]],"date-time":"2020-08-02T21:14:36Z","timestamp":1596402876000},"page":"2195-2212","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Predicting rice phenotypes with meta and multi-target learning"],"prefix":"10.1007","volume":"109","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1178-611X","authenticated-orcid":false,"given":"Oghenejokpeme I.","family":"Orhobor","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nickolai N.","family":"Alexandrov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7208-4387","authenticated-orcid":false,"given":"Ross D.","family":"King","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,8,2]]},"reference":[{"key":"5881_CR1","doi-asserted-by":"crossref","unstructured":"Abraham, Z., Tan, P. N., Winkler, J., Zhong, S., Liszewska, M., et\u00a0al. (2013). Position preserving multi-output prediction. In Joint European conference on machine learning and knowledge discovery in databases (pp. 320\u2013335). Springer.","DOI":"10.1007\/978-3-642-40991-2_21"},{"issue":"Aug","key":"5881_CR2","first-page":"2367","volume":"13","author":"T Aho","year":"2012","unstructured":"Aho, T., \u017denko, B., D\u017eeroski, S., & Elomaa, T. (2012). Multi-target regression with rule ensembles. Journal of Machine Learning Research, 13(Aug), 2367\u20132407.","journal-title":"Journal of Machine Learning Research"},{"issue":"D1","key":"5881_CR3","doi-asserted-by":"publisher","first-page":"D1023","DOI":"10.1093\/nar\/gku1039","volume":"43","author":"N Alexandrov","year":"2015","unstructured":"Alexandrov, N., Tai, S., Wang, W., Mansueto, L., Palis, K., Fuentes, R. R., et al. (2015). SNP-Seek database of SNPs derived from 3000 rice genomes. Nucleic Acids Research, 43(D1), D1023\u2013D1027.","journal-title":"Nucleic Acids Research"},{"issue":"3","key":"5881_CR4","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","volume":"46","author":"NS Altman","year":"1992","unstructured":"Altman, N. S. (1992). An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician, 46(3), 175\u2013185.","journal-title":"The American Statistician"},{"key":"5881_CR5","doi-asserted-by":"crossref","unstructured":"Appice, A., & D\u017eeroski, S. (2007). Stepwise induction of multi-target model trees. In European conference on machine learning (pp. 502\u2013509). Springer.","DOI":"10.1007\/978-3-540-74958-5_46"},{"issue":"Feb","key":"5881_CR6","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(Feb), 281\u2013305.","journal-title":"Journal of Machine Learning Research"},{"issue":"5","key":"5881_CR7","first-page":"216","volume":"5","author":"H Borchani","year":"2015","unstructured":"Borchani, H., Varando, G., Bielza, C., & Larra\u00f1aga, P. (2015). A survey on multi-output regression. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 5(5), 216\u2013233.","journal-title":"Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"},{"issue":"3","key":"5881_CR8","doi-asserted-by":"publisher","first-page":"369","DOI":"10.4310\/SII.2009.v2.n3.a10","volume":"2","author":"P Breheny","year":"2009","unstructured":"Breheny, P., & Huang, J. (2009). Penalized methods for bi-level variable selection. Statistics and its Interface, 2(3), 369.","journal-title":"Statistics and its Interface"},{"issue":"1","key":"5881_CR9","first-page":"49","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman, L. (1996). Stacked regressions. Machine Learning, 24(1), 49\u201364.","journal-title":"Machine Learning"},{"issue":"1","key":"5881_CR10","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. Machine Learning, 45(1), 5\u201332.","journal-title":"Machine Learning"},{"key":"5881_CR11","doi-asserted-by":"crossref","unstructured":"Caruana, R., Niculescu-Mizil, A., Crew, G., & Ksikes, A. (2004). Ensemble selection from libraries of models. In Proceedings of the twenty-first international conference on machine learning (p.\u00a018). ACM.","DOI":"10.1145\/1015330.1015432"},{"key":"5881_CR12","unstructured":"Chen, T., & He, T. (2015). xgboost: eXtreme Gradient Boosting. R package version 0.4-2 (2015)"},{"key":"5881_CR13","unstructured":"Cortes, C., Mohri, M., & Rostamizadeh, A. (2009). Learning non-linear combinations of kernels. In Y. Bengio, D. Schuurmans, J. D. Lafferty, C. K. I. Williams, & A. Culotta (Eds.), Advances in neural information processing systems 22 (pp. 396\u2013404). Curran Associates, Inc."},{"issue":"3","key":"5881_CR14","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273\u2013297.","journal-title":"Machine Learning"},{"key":"5881_CR15","doi-asserted-by":"crossref","unstructured":"D\u017eeroski, S., & \u017denko, B. (2002). Stacking with multi-response model trees. In International workshop on multiple classifier systems (pp. 201\u2013211). Springer.","DOI":"10.1007\/3-540-45428-4_20"},{"issue":"3","key":"5881_CR16","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1023\/B:MACH.0000015881.36452.6e","volume":"54","author":"S D\u017eeroski","year":"2004","unstructured":"D\u017eeroski, S., & \u017denko, B. (2004). Is combining classifiers with stacking better than selecting the best one? Machine Learning, 54(3), 255\u2013273.","journal-title":"Machine Learning"},{"issue":"3","key":"5881_CR17","doi-asserted-by":"publisher","first-page":"250","DOI":"10.3835\/plantgenome2011.08.0024","volume":"4","author":"JB Endelman","year":"2011","unstructured":"Endelman, J. B. (2011). Ridge regression and other kernels for genomic selection with R package rrBLUP. The Plant Genome, 4(3), 250\u2013255.","journal-title":"The Plant Genome"},{"key":"5881_CR18","unstructured":"Friedman, J., Hastie, T., & Tibshirani, R. (2010). A note on the group lasso and a sparse group lasso. arXiv preprint arXiv:1001.0736"},{"key":"5881_CR19","doi-asserted-by":"crossref","unstructured":"Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189\u20131232.","DOI":"10.1214\/aos\/1013203451"},{"issue":"8","key":"5881_CR20","doi-asserted-by":"publisher","first-page":"e0136594","DOI":"10.1371\/journal.pone.0136594","volume":"10","author":"C Grenier","year":"2015","unstructured":"Grenier, C., Cao, T. V., Ospina, Y., Quintero, C., Ch\u00e2tel, M. H., Tohme, J., et al. (2015). Accuracy of genomic selection in a rice synthetic population developed for recurrent selection breeding. PloS ONE, 10(8), e0136594.","journal-title":"PloS ONE"},{"key":"5881_CR21","doi-asserted-by":"crossref","unstructured":"Grinberg, N. F., Lovatt, A., Hegarty, M., Lovatt, A., Sk\u00f8t, K. P., Kelly, R., et\u00a0al. (2016). Implementation of genomic prediction in Lolium perenne (L.) breeding populations. Frontiers in Plant Science, 7, 133.","DOI":"10.3389\/fpls.2016.00133"},{"key":"5881_CR22","doi-asserted-by":"publisher","unstructured":"Grinberg, N. F., Orhobor, O. I., & King, R. D. (2019). An evaluationof machine-learning for predicting phenotype: Studies in yeast, rice, and wheat. Machine Learning. https:\/\/doi.org\/10.1007\/s10994-019-05848-5.","DOI":"10.1007\/s10994-019-05848-5"},{"key":"5881_CR23","doi-asserted-by":"crossref","unstructured":"Hainmueller, J., & Hazlett, C. (2014). Kernel regularized least squares: Reducing misspecification bias with a flexible and interpretable machine learning approach. Political Analysis, 22(2), 143\u2013168.","DOI":"10.1093\/pan\/mpt019"},{"issue":"12","key":"5881_CR24","doi-asserted-by":"publisher","first-page":"1400","DOI":"10.1016\/j.conengprac.2012.08.006","volume":"20","author":"Z Han","year":"2012","unstructured":"Han, Z., Liu, Y., Zhao, J., & Wang, W. (2012). Real time prediction for converter gas tank levels based on multi-output least square support vector regressor. Control Engineering Practice, 20(12), 1400\u20131409.","journal-title":"Control Engineering Practice"},{"issue":"2","key":"5881_CR25","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1093\/biomet\/asp020","volume":"96","author":"J Huang","year":"2009","unstructured":"Huang, J., Ma, S., Xie, H., & Zhang, C. H. (2009). A group bridge approach for variable selection. Biometrika, 96(2), 339\u2013355.","journal-title":"Biometrika"},{"issue":"3","key":"5881_CR26","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1080\/10618600.1996.10474713","volume":"5","author":"R Ihaka","year":"1996","unstructured":"Ihaka, R., & Gentleman, R. (1996). R: A language for data analysis and graphics. Journal of Computational and Graphical Statistics, 5(3), 299\u2013314.","journal-title":"Journal of Computational and Graphical Statistics"},{"key":"5881_CR27","doi-asserted-by":"crossref","unstructured":"Ikonomovska, E., Gama, J., & D\u017eeroski, S. (2011). Incremental multi-target model trees for data streams. In Proceedings of the 2011 ACM symposium on applied computing (pp. 988\u2013993). ACM.","DOI":"10.1145\/1982185.1982402"},{"key":"5881_CR28","doi-asserted-by":"crossref","unstructured":"Jahrer, M., T\u00f6scher, A., & Legenstein, R. (2010). Combining predictions for accurate recommender systems. In Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 693\u2013702). ACM.","DOI":"10.1145\/1835804.1835893"},{"key":"5881_CR29","doi-asserted-by":"crossref","unstructured":"Jolliffe, I. T. (1982). A note on the use of principal components in regression. Journal of the Royal Statistical Society: Series C (Applied Statistics), 31(3), 300\u2013303.","DOI":"10.2307\/2348005"},{"issue":"1","key":"5881_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1006\/inco.1996.2612","volume":"132","author":"J Kivinen","year":"1997","unstructured":"Kivinen, J., & Warmuth, M. K. (1997). Exponentiated gradient versus gradient descent for linear predictors. Information and Computation, 132(1), 1\u201363.","journal-title":"Information and Computation"},{"issue":"8","key":"5881_CR31","doi-asserted-by":"publisher","first-page":"1159","DOI":"10.1016\/j.ecolmodel.2009.01.037","volume":"220","author":"D Kocev","year":"2009","unstructured":"Kocev, D., D\u017eeroski, S., White, M. D., Newell, G. R., & Griffioen, P. (2009). Using single-and multi-target regression trees and ensembles to model a compound index of vegetation condition. Ecological Modelling, 220(8), 1159\u20131168.","journal-title":"Ecological Modelling"},{"issue":"Jan","key":"5881_CR32","first-page":"27","volume":"5","author":"GR Lanckriet","year":"2004","unstructured":"Lanckriet, G. R., Cristianini, N., Bartlett, P., Ghaoui, L. E., & Jordan, M. I. (2004). Learning the kernel matrix with semidefinite programming. Journal of Machine Learning Research, 5(Jan), 27\u201372.","journal-title":"Journal of Machine Learning Research"},{"key":"5881_CR33","volume-title":"Rice Almanac: Source book for one of the most important economic activities on earth","author":"J Maclean","year":"2013","unstructured":"Maclean, J., Hardy, B., & Hettel, G. (2013). Rice Almanac: Source book for one of the most important economic activities on earth. Los Banos: International Rice Research Institute."},{"issue":"1","key":"5881_CR34","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1145\/2379776.2379786","volume":"45","author":"J Mendes-Moreira","year":"2012","unstructured":"Mendes-Moreira, J., Soares, C., Jorge, A. M., & Sousa, J. F. D. (2012). Ensemble approaches for regression: A survey. ACM Computing Surveys (CSUR), 45(1), 10.","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"5881_CR35","unstructured":"Merz, C. J. (1998). Classification and regression by combining models. Ph.D. thesis, University of California Irvine."},{"issue":"10","key":"5881_CR36","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1002\/cem.1246","volume":"23","author":"W Ni","year":"2009","unstructured":"Ni, W., Brown, S. D., & Man, R. (2009). Stacked partial least squares regression analysis for spectral calibration and prediction. Journal of Chemometrics, 23(10), 505\u2013517.","journal-title":"Journal of Chemometrics"},{"key":"5881_CR37","doi-asserted-by":"crossref","unstructured":"Ogutu, J.O., & Piepho, H.P. (2014). Regularized group regression methods for genomic prediction: Bridge, MCP, SCAD, group bridge, group lasso, sparse group lasso, group MCP and group SCAD. In BMC Proceedings (vol.\u00a08, p.\u00a0S7). BioMed Central.","DOI":"10.1186\/1753-6561-8-S5-S7"},{"issue":"1","key":"5881_CR38","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/s00122-014-2411-y","volume":"128","author":"A Onogi","year":"2015","unstructured":"Onogi, A., Ideta, O., Inoshita, Y., Ebana, K., Yoshioka, T., Yamasaki, M., et al. (2015). Exploring the areas of applicability of whole-genome prediction methods for asian rice (Oryza sativa L.). Theoretical and Applied Genetics, 128(1), 41\u201353.","journal-title":"Theoretical and Applied Genetics"},{"key":"5881_CR39","doi-asserted-by":"crossref","unstructured":"Orhobor, O. I., Alexandrov, N. N., & King, R. D. (2018). Predicting rice phenotypes with meta-learning. In International conference on discovery science (pp. 144\u2013158). Springer.","DOI":"10.1007\/978-3-030-01771-2_10"},{"issue":"3\u20134","key":"5881_CR40","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1080\/095400996116848","volume":"8","author":"B Parmanto","year":"1996","unstructured":"Parmanto, B., Munro, P. W., & Doyle, H. R. (1996). Reducing variance of committee prediction with resampling techniques. Connection Science, 8(3\u20134), 405\u2013426.","journal-title":"Connection Science"},{"issue":"3","key":"5881_CR41","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1086\/519795","volume":"81","author":"S Purcell","year":"2007","unstructured":"Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A., Bender, D., et al. (2007). PLINK: a tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics, 81(3), 559\u2013575.","journal-title":"The American Journal of Human Genetics"},{"issue":"6","key":"5881_CR42","doi-asserted-by":"publisher","first-page":"e66428","DOI":"10.1371\/journal.pone.0066428","volume":"8","author":"DK Ray","year":"2013","unstructured":"Ray, D. K., Mueller, N. D., West, P. C., & Foley, J. A. (2013). Yield trends are insufficient to double global crop production by 2050. PloS ONE, 8(6), e66428.","journal-title":"PloS ONE"},{"key":"5881_CR43","doi-asserted-by":"crossref","unstructured":"Rooney, N., Patterson, D., Anand, S., & Tsymbal, A. (2004). Dynamic integration of regression models. In International workshop on multiple classifier systems (Vol. 3077, pp. 164\u2013173).","DOI":"10.1007\/978-3-540-25966-4_16"},{"issue":"3","key":"5881_CR44","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1534\/g3.112.005363","volume":"3","author":"JE Rutkoski","year":"2013","unstructured":"Rutkoski, J. E., Poland, J., Jannink, J., & Sorrells, M. E. (2013). Imputation of unordered markers and the impact on genomic selection accuracy. G3: Genes, Genomes, Genetics, 3(3), 427\u2013439.","journal-title":"G3: Genes, Genomes, Genetics"},{"issue":"8","key":"5881_CR45","doi-asserted-by":"publisher","first-page":"2298","DOI":"10.1109\/TSP.2004.831028","volume":"52","author":"M S\u00e1nchez-Fern\u00e1ndez","year":"2004","unstructured":"S\u00e1nchez-Fern\u00e1ndez, M., de Prado-Cumplido, M., Arenas-Garc\u00eda, J., & P\u00e9rez-Cruz, F. (2004). Svm multiregression for nonlinear channel estimation in multiple-input multiple-output systems. IEEE Transactions on Signal Processing, 52(8), 2298\u20132307.","journal-title":"IEEE Transactions on Signal Processing"},{"issue":"Jul","key":"5881_CR46","first-page":"1531","volume":"7","author":"S Sonnenburg","year":"2006","unstructured":"Sonnenburg, S., R\u00e4tsch, G., Sch\u00e4fer, C., & Sch\u00f6lkopf, B. (2006). Large scale multiple kernel learning. Journal of Machine Learning Research, 7(Jul), 1531\u20131565.","journal-title":"Journal of Machine Learning Research"},{"issue":"2","key":"5881_CR47","doi-asserted-by":"publisher","first-page":"e1004982","DOI":"10.1371\/journal.pgen.1004982","volume":"11","author":"J Spindel","year":"2015","unstructured":"Spindel, J., Begum, H., Akdemir, D., Virk, P., Collard, B., Redo\u00f1a, E., et al. (2015). Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines. PLoS Genetics, 11(2), e1004982.","journal-title":"PLoS Genetics"},{"key":"5881_CR48","unstructured":"Spyromitros-Xioufis, E., Tsoumakas, G., Groves, W., Vlahavas, I. (2012). Multi-label classification methods for multi-target regression (pp. 1159\u20131168). arXiv preprint arXiv:1211.6581."},{"issue":"9","key":"5881_CR49","doi-asserted-by":"publisher","first-page":"817","DOI":"10.1038\/nclimate2317","volume":"4","author":"AP Tai","year":"2014","unstructured":"Tai, A. P., Martin, M. V., & Heald, C. L. (2014). Threat to future global food security from climate change and ozone air pollution. Nature Climate Change, 4(9), 817\u2013821.","journal-title":"Nature Climate Change"},{"key":"5881_CR50","doi-asserted-by":"crossref","unstructured":"Tibshirani, R. (1996). Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267\u2013288.","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"5881_CR51","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1613\/jair.594","volume":"10","author":"KM Ting","year":"1999","unstructured":"Ting, K. M., & Witten, I. H. (1999). Issues in stacked generalization. Journal of Artificial Intelligence Research, 10, 271\u2013289.","journal-title":"Journal of Artificial Intelligence Research"},{"key":"5881_CR52","doi-asserted-by":"crossref","unstructured":"Tsoumakas, G., Spyromitros-Xioufis, E., Vrekou, A., & Vlahavas, I. (2014). Multi-target regression via random linear target combinations. In Joint European conference on machine learning and knowledge discovery in databases (pp. 225\u2013240). Springer.","DOI":"10.1007\/978-3-662-44845-8_15"},{"issue":"4","key":"5881_CR53","doi-asserted-by":"publisher","first-page":"804","DOI":"10.1109\/LGRS.2011.2109934","volume":"8","author":"D Tuia","year":"2011","unstructured":"Tuia, D., Verrelst, J., Alonso, L., P\u00e9rez-Cruz, F., & Camps-Valls, G. (2011). Multioutput support vector regression for remote sensing biophysical parameter estimation. IEEE Geoscience and Remote Sensing Letters, 8(4), 804\u2013808.","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"key":"5881_CR54","unstructured":"UN. (2015). U.N.: World Population Prospects: The 2015 Revision, Key Findings and Advance Tables. Working Paper, No. ESA\/P\/WP. 241."},{"key":"5881_CR55","doi-asserted-by":"crossref","unstructured":"Van der Laan, M. J., Polley, E. C., & Hubbard, A. E. (2007). Super learner. Statistical Applications in Genetics and Molecular Biology, 6(1). https:\/\/www.degruyter.com\/view\/journals\/sagmb\/6\/1\/article-sagmb.2007.6.1.1309.xml.xml.","DOI":"10.2202\/1544-6115.1309"},{"key":"5881_CR56","unstructured":"Xu, C., Tao, D., & Xu, C. (2013). A survey on multi-view learning. arXiv preprint arXiv:1304.5634"},{"issue":"2","key":"5881_CR57","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1016\/j.chemolab.2007.02.001","volume":"87","author":"L Xu","year":"2007","unstructured":"Xu, L., Jiang, J. H., Zhou, Y. P., Wu, H. L., Shen, G. L., & Yu, R. Q. (2007). MCCV stacked regression for model combination and fast spectral interval selection in multivariate calibration. Chemometrics and Intelligent Laboratory Systems, 87(2), 226\u2013230.","journal-title":"Chemometrics and Intelligent Laboratory Systems"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-020-05881-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-020-05881-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-020-05881-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,11]],"date-time":"2024-08-11T06:05:13Z","timestamp":1723356313000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-020-05881-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,2]]},"references-count":57,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2020,11]]}},"alternative-id":["5881"],"URL":"https:\/\/doi.org\/10.1007\/s10994-020-05881-9","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"type":"print","value":"0885-6125"},{"type":"electronic","value":"1573-0565"}],"subject":[],"published":{"date-parts":[[2020,8,2]]},"assertion":[{"value":"11 July 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 December 2019","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 May 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 August 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}