{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:54:41Z","timestamp":1760122481935},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2015,10,1]],"date-time":"2015-10-01T00:00:00Z","timestamp":1443657600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2016,11]]},"DOI":"10.1007\/s00521-015-2065-y","type":"journal-article","created":{"date-parts":[[2015,10,1]],"date-time":"2015-10-01T07:17:43Z","timestamp":1443683863000},"page":"2289-2304","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Classifying component failures of a hybrid electric vehicle fleet based on load spectrum data"],"prefix":"10.1007","volume":"27","author":[{"given":"Philipp","family":"Bergmeir","sequence":"first","affiliation":[]},{"given":"Christof","family":"Nitsche","sequence":"additional","affiliation":[]},{"given":"J\u00fcrgen","family":"Nonnast","sequence":"additional","affiliation":[]},{"given":"Michael","family":"Bargende","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2015,10,1]]},"reference":[{"issue":"1","key":"2065_CR1","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. doi: 10.1023\/A:1010933404324","journal-title":"Mach Learn"},{"key":"2065_CR2","volume-title":"Classification and regression trees","author":"L Breiman","year":"1984","unstructured":"Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. Chapman and Hall\/CRC, New York"},{"issue":"1","key":"2065_CR3","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1023\/A:1022607123649","volume":"19","author":"C Brodley","year":"1995","unstructured":"Brodley C, Utgoff P (1995) Multivariate decision trees. Mach Learn 19(1):45\u201377. doi: 10.1023\/A:1022607123649","journal-title":"Mach Learn"},{"key":"2065_CR4","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-642-13164-6","volume-title":"Z\u00e4hlverfahren und Lastannahme in der Betriebsfestigkeit","author":"M K\u00f6hler","year":"2012","unstructured":"K\u00f6hler M, Jenne S, P\u00f6tter K, Zenner H (2012) Z\u00e4hlverfahren und Lastannahme in der Betriebsfestigkeit. Springer, Berlin"},{"key":"2065_CR5","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.ins.2013.07.007","volume":"250","author":"V L\u00f3pez","year":"2013","unstructured":"L\u00f3pez V, Fern\u00e1ndez A, Garc\u00eda S, Palade V, Herrera F (2013) An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Inf Sci 250:113\u2013141. doi: 10.1016\/j.ins.2013.07.007","journal-title":"Inf Sci"},{"issue":"14","key":"2065_CR6","doi-asserted-by":"publisher","first-page":"2225","DOI":"10.1016\/j.patrec.2010.03.014","volume":"31","author":"R Genuer","year":"2010","unstructured":"Genuer R, Poggi JM, Tuleau-Malot C (2010) Variable selection using random forests. Pattern Recogn Lett 31(14):2225\u20132236. doi: 10.1016\/j.patrec.2010.03.014","journal-title":"Pattern Recogn Lett"},{"key":"2065_CR7","unstructured":"Breiman L, Cutler A (2015) Random forests-classification description. Department of Statistics Homepage. http:\/\/www.stat.berkeley.edu\/~breiman\/RandomForests\/cc_home.htm . Accessed 15 Jan 2015"},{"key":"2065_CR8","unstructured":"Bergmeir P, Nitsche C, Nonnast J, Bargende M, Antony P, Keller U (2014) Klassifikationsverfahren zur Identifikation von Korrelationen zwischen Antriebsstrangbelastungen und Hybridkomponentenfehlern einer Hybridfahrzeugflotte. Technical report, Universit\u00e4t Stuttgart"},{"key":"2065_CR9","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4757-2440-0","volume-title":"The nature of statistical learning theory","author":"VN Vapnik","year":"1995","unstructured":"Vapnik VN (1995) The nature of statistical learning theory. Springer, New York"},{"key":"2065_CR10","doi-asserted-by":"publisher","unstructured":"Bergmeir P, Nitsche C, Nonnast J, Bargende M, Antony P, Keller U (2014) Using balanced random forests on load spectrum data for classifying component failures of a hybrid electric vehicle fleet. In: 13th international conference on machine learning and applications (ICMLA 2014), pp 397\u2013404. doi: 10.1109\/ICMLA.2014.71","DOI":"10.1109\/ICMLA.2014.71"},{"issue":"2","key":"2065_CR11","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1007\/s10115-006-0063-1","volume":"12","author":"O Gusikhin","year":"2007","unstructured":"Gusikhin O, Rychtyckyj N, Filev D (2007) Intelligent systems in the automotive industry: applications and trends. Knowl Inf Syst 12(2):147\u2013168","journal-title":"Knowl Inf Syst"},{"issue":"1","key":"2065_CR12","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.cie.2008.11.006","volume":"57","author":"J Buddhakulsomsiri","year":"2009","unstructured":"Buddhakulsomsiri J, Zakarian A (2009) Sequential pattern mining algorithm for automotive warranty data. Comput Ind Eng 57(1):137\u2013147. doi: 10.1016\/j.cie.2008.11.006","journal-title":"Comput Ind Eng"},{"key":"2065_CR13","doi-asserted-by":"crossref","unstructured":"Frisk E, Krysander M, Larsson E (2014) Data-driven lead-acid battery prognostics using random survival forests. In: Proceedings of the 2nd European conference of the PHM society (PHME14)","DOI":"10.36001\/phmconf.2014.v6i1.2370"},{"key":"2065_CR14","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.engappai.2015.02.009","volume":"41","author":"R Prytz","year":"2015","unstructured":"Prytz R, Nowaczyk S, Rgnvaldsson T, Byttner S (2015) Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data. Eng Appl Artif Intell 41:139\u2013150. doi: 10.1016\/j.engappai.2015.02.009","journal-title":"Eng Appl Artif Intell"},{"key":"2065_CR15","volume-title":"Fatigue testing and analysis: theory and practice","author":"Y Lee","year":"2011","unstructured":"Lee Y, Pan J, Hathaway R, Barkey M (2011) Fatigue testing and analysis: theory and practice. Elsevier Science, Amsterdam"},{"key":"2065_CR16","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/B0-08-043749-4\/04029-5","volume-title":"Comprehensive structural integrity","author":"Y Kondo","year":"2003","unstructured":"Kondo Y (2003) 4.10-fatigue under variable amplitude loading. In: Karihaloo IMR (ed) Comprehensive structural integrity. Pergamon, Oxford, pp 253\u2013279"},{"key":"2065_CR17","unstructured":"Saha B, Goebel K (2007) Battery data set. NASA Ames Prognostics Data Repository. http:\/\/ti.arc.nasa.gov\/tech\/dash\/pcoe\/prognostic-data-repository\/#battery . Accessed 12 Jan 2015"},{"key":"2065_CR18","volume-title":"C4.5: programs for machine learning","author":"JR Quinlan","year":"1993","unstructured":"Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Francisco"},{"key":"2065_CR19","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.ins.2014.03.043","volume":"285","author":"S R\u00edo del","year":"2014","unstructured":"del R\u00edo S, L\u00f3pez V, Ben\u00edtez JM, Herrera F (2014) On the use of MapReduce for imbalanced big data using random forest. Inf Sci 285:112\u2013137. doi: 10.1016\/j.ins.2014.03.043","journal-title":"Inf Sci"},{"key":"2065_CR20","series-title":"Springer Series in Statistics","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The elements of statistical learning: data mining, inference, and prediction","author":"T Hastie","year":"2009","unstructured":"Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction, 2nd edn., Springer Series in StatisticsSpringer, SpringerBerlin","edition":"2"},{"key":"2065_CR21","unstructured":"Liaw A, Wiener M (2002) Classification and Regression by randomForest. R News 2(3):18\u201322. http:\/\/CRAN.R-project.org\/doc\/Rnews\/"},{"issue":"1","key":"2065_CR22","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.media.2014.09.007","volume":"19","author":"M Schneider","year":"2015","unstructured":"Schneider M, Hirsch S, Weber B, Sz\u00e9kely G, Menze BH (2015) Joint 3-D vessel segmentation and centerline extraction using oblique Hough forests with steerable filters. Med Image Anal 19(1):220\u2013249. doi: 10.1016\/j.media.2014.09.007","journal-title":"Med Image Anal"},{"key":"2065_CR23","doi-asserted-by":"crossref","unstructured":"Menze BH, Kelm BM, Splitthoff DN, Koethe U, Hamprecht FA (2011) On oblique random forests. In: Gunopulos D, Hofmann T, Malerba D, Vazirgiannis M (eds) Machine learning and knowledge discovery in databases. Springer, Berlin, pp 453\u2013469","DOI":"10.1007\/978-3-642-23783-6_29"},{"key":"2065_CR24","doi-asserted-by":"publisher","unstructured":"Barros R, Cerri R, Jaskowiak P, de\u00a0Carvalho A (2011) A bottom-up oblique decision tree induction algorithm. In: 11th international conference on intelligent systems design and applications (ISDA 2011), pp 450\u2013456. doi: 10.1109\/ISDA.2011.6121697","DOI":"10.1109\/ISDA.2011.6121697"},{"issue":"1","key":"2065_CR25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1613\/jair.63","volume":"2","author":"SK Murthy","year":"1994","unstructured":"Murthy SK, Kasif S, Salzberg S (1994) A system for induction of oblique decision trees. J Artif Intell Res 2(1):1\u201332","journal-title":"J Artif Intell Res"},{"key":"2065_CR26","unstructured":"Parfionovas A (2013) Enhancement of random forests using trees with oblique splits. Dissertation, Utah State University. http:\/\/digitalcommons.usu.edu\/etd\/1508 . Accessed 07 Jan 2015"},{"key":"2065_CR27","doi-asserted-by":"crossref","unstructured":"Friedman JH, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J Stat. Softw 33(1):1\u201322. http:\/\/www.jstatsoft.org\/v33\/i01","DOI":"10.18637\/jss.v033.i01"},{"key":"2065_CR28","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","volume":"67","author":"H Zou","year":"2005","unstructured":"Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc Ser B 67:301\u2013320. doi: 10.1111\/j.1467-9868.2005.00503.x","journal-title":"J R Stat Soc Ser B"},{"issue":"1","key":"2065_CR29","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1080\/00401706.2000.10485983","volume":"42","author":"AE Hoerl","year":"2000","unstructured":"Hoerl AE, Kennard RW (2000) Ridge regression: biased estimation for nonorthogonal problems. Technometrics 42(1):80\u201386. doi: 10.1080\/00401706.2000.10485983","journal-title":"Technometrics"},{"key":"2065_CR30","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1111\/j.1467-9868.2011.00771.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B 58:267\u2013288. doi: 10.1111\/j.1467-9868.2011.00771.x","journal-title":"J R Stat Soc Ser B"},{"key":"2065_CR31","unstructured":"Truong AKY (2009) Fast growing and interpretable oblique trees via logistic regression models. Dissertation, University of Oxford. http:\/\/ora.ox.ac.uk\/objects\/uuid:e0de0156-da01-4781-85c5-8213f5004f10 . Accessed 25 Jan 2015"},{"issue":"2","key":"2065_CR32","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/S0169-7439(01)00153-8","volume":"58","author":"H Martens","year":"2001","unstructured":"Martens H (2001) Reliable and relevant modelling of real world data: a personal account of the development of PLS regression. Chemom Intell Lab Syst 58(2):85\u201395. doi: 10.1016\/S0169-7439(01)00153-8","journal-title":"Chemom Intell Lab Syst"},{"issue":"2","key":"2065_CR33","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/S0169-7439(01)00152-6","volume":"58","author":"S Wold","year":"2001","unstructured":"Wold S (2001) Personal memories of the early PLS development. Chemom Intell Lab Syst 58(2):83\u201384. doi: 10.1016\/S0169-7439(01)00152-6","journal-title":"Chemom Intell Lab Syst"},{"key":"2065_CR34","doi-asserted-by":"crossref","unstructured":"Mevik BH, Wehrens R (2007) The pls package: principal component and partial least squares regression in R. J Stat Softw 18(2):1\u201324. http:\/\/www.jstatsoft.org\/v18\/i02","DOI":"10.18637\/jss.v018.i02"},{"issue":"1","key":"2065_CR35","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1002\/(SICI)1099-128X(199701)11:1<73::AID-CEM435>3.0.CO;2-#","volume":"11","author":"BS Dayal","year":"1997","unstructured":"Dayal BS, MacGregor JF (1997) Improved PLS algorithms. J Chemom 11(1):73\u201385","journal-title":"J Chemom"},{"key":"2065_CR36","doi-asserted-by":"publisher","unstructured":"Do TN, Lenca P, Lallich S, Pham NK (2010) Classifying very-high-dimensional data with random forests of oblique decision trees. In: Guillet F, Ritschard G, Zighed D, Briand H (eds) Advances in knowledge discovery and management, studies in computational intelligence, vol 292. Springer, Berlin, pp 39\u201355. doi: 10.1007\/978-3-642-00580-0_3","DOI":"10.1007\/978-3-642-00580-0_3"},{"key":"2065_CR37","doi-asserted-by":"publisher","unstructured":"Fung G, Mangasarian OL (2001) Proximal support vector machine classifiers. In: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining, ACM, New York, NY, USA, KDD \u201901, pp 77\u201386. doi: 10.1145\/502512.502527","DOI":"10.1145\/502512.502527"},{"key":"2065_CR38","unstructured":"Chen C, Liaw A, Breiman L (2004) Using random forest to learn imbalanced data. Technical report, Department of Statistics, University of Berkeley. http:\/\/www.stat.berkeley.edu\/users\/chenchao\/666.pdf . Accessed 29 Dec 2014"},{"issue":"1","key":"2065_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-7-3","volume":"7","author":"R D\u00edaz-Uriarte","year":"2006","unstructured":"D\u00edaz-Uriarte R, de Alvarez Andr\u00e9s S (2006) Gene selection and classification of microarray data using random forest. BMC Bioinform 7(1):1\u201313. doi: 10.1186\/1471-2105-7-3","journal-title":"BMC Bioinform"},{"key":"2065_CR40","unstructured":"Menze B, Splitthoff N (2012) obliqueRF: oblique random forests from recursive linear model splits. http:\/\/CRAN.R-project.org\/package=obliqueRF . R package version 0.3"},{"key":"2065_CR41","doi-asserted-by":"crossref","unstructured":"Kuhn M (2008) Building predictive models in r using the caret package. J Stat Softw 28(5):1\u201326. http:\/\/www.jstatsoft.org\/v28\/i05","DOI":"10.18637\/jss.v028.i05"},{"key":"2065_CR42","unstructured":"Kuhn M, Wing J, Weston S, Williams A, Keefer C, Engelhardt A, Cooper T, Mayer Z, the R Core Team (2014) caret: Classification and regression training. http:\/\/CRAN.R-project.org\/package=caret . R package version 6.0-24"},{"key":"2065_CR43","doi-asserted-by":"publisher","unstructured":"Brodersen K, Ong CS, Stephan K, Buhmann J (2010) The balanced accuracy and its posterior distribution. In: 20th international conference on pattern recognition (ICPR 2010), pp 3121\u20133124. doi: 10.1109\/ICPR.2010.764","DOI":"10.1109\/ICPR.2010.764"},{"key":"2065_CR44","doi-asserted-by":"publisher","unstructured":"Dahinden C (2006) Classification with tree-based ensembles applied to the WCCI 2006 performance prediction challenge datasets. In: International joint conference on neural networks (IJCNN \u201906), pp 1669\u20131672. doi: 10.1109\/IJCNN.2006.246635","DOI":"10.1109\/IJCNN.2006.246635"},{"key":"2065_CR45","doi-asserted-by":"crossref","unstructured":"Kuhn M, Johnson K (2013) Applied predictive modeling. SpringerLink: B\u00fccher, Springer. http:\/\/books.google.de\/books?id=xYRDAAAAQBAJ","DOI":"10.1007\/978-1-4614-6849-3"},{"key":"2065_CR46","unstructured":"Dem\u0161ar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1\u201330. http:\/\/dl.acm.org\/citation.cfm?id=1248547.1248548"},{"issue":"10","key":"2065_CR47","doi-asserted-by":"publisher","first-page":"2044","DOI":"10.1016\/j.ins.2009.12.010","volume":"180","author":"S Garc\u00eda","year":"2010","unstructured":"Garc\u00eda S, Fern\u00e1ndez A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Inf Sci 180(10):2044\u20132064. doi: 10.1016\/j.ins.2009.12.010","journal-title":"Inf Sci"},{"key":"2065_CR48","doi-asserted-by":"crossref","unstructured":"Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200):675\u2013701. http:\/\/www.jstor.org\/stable\/2279372","DOI":"10.1080\/01621459.1937.10503522"},{"key":"2065_CR49","doi-asserted-by":"crossref","unstructured":"Friedman M (1940) A comparison of alternative tests of significance for the problem of m rankings. Ann Math Stat 11(1):86\u201392. http:\/\/www.jstor.org\/stable\/2235971","DOI":"10.1214\/aoms\/1177731944"},{"issue":"4","key":"2065_CR50","doi-asserted-by":"publisher","first-page":"800","DOI":"10.1093\/biomet\/75.4.800","volume":"75","author":"Y Hochberg","year":"1988","unstructured":"Hochberg Y (1988) A sharper Bonferroni procedure for multiple tests of significance. Biometrika 75(4):800\u2013802. doi: 10.1093\/biomet\/75.4.800","journal-title":"Biometrika"},{"issue":"6","key":"2065_CR51","doi-asserted-by":"crossref","first-page":"80","DOI":"10.2307\/3001968","volume":"1","author":"F Wilcoxon","year":"1945","unstructured":"Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1(6):80\u201383","journal-title":"Biom Bull"},{"issue":"6","key":"2065_CR52","doi-asserted-by":"crossref","first-page":"1663","DOI":"10.1109\/TCBB.2012.117","volume":"9","author":"O Irsoy","year":"2012","unstructured":"Irsoy O, Yildiz OT, Alpaydin E (2012) Design and analysis of classifier learning experiments in bioinformatics: survey and case studies. IEEE\/ACM Trans Comput Biol Bioinform 9(6):1663\u20131675","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"1\u20134","key":"2065_CR53","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/S0925-2312(01)00653-1","volume":"48","author":"J Pizarro","year":"2002","unstructured":"Pizarro J, Guerrero E, Galindo PL (2002) Multiple comparison procedures applied to model selection. Neurocomputing 48(1\u20134):155\u2013173. doi: 10.1016\/S0925-2312(01)00653-1","journal-title":"Neurocomputing"},{"key":"2065_CR54","unstructured":"Herb F (2010) Alterungsmechanismen in Lithium-Ionen-Batterien und PEM-Brennstoffzellen und deren Einfluss auf die Eigenschaften von daraus bestehenden hybrid-systemen. Dissertation, University Ulm, Faculty of Natural Sciences. http:\/\/vts.uni-ulm.de\/doc.asp?id=7404 . Accessed 04 Jan 2015"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-015-2065-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-015-2065-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-015-2065-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-015-2065-y","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,11]],"date-time":"2024-06-11T14:00:41Z","timestamp":1718114441000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-015-2065-y"}},"subtitle":["Balanced random forest approaches employing uni- and multivariate decision trees"],"short-title":[],"issued":{"date-parts":[[2015,10,1]]},"references-count":54,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2016,11]]}},"alternative-id":["2065"],"URL":"https:\/\/doi.org\/10.1007\/s00521-015-2065-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,10,1]]}}}