{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T19:28:44Z","timestamp":1775330924483,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2006,3,2]],"date-time":"2006-03-02T00:00:00Z","timestamp":1141257600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2006,4]]},"DOI":"10.1007\/s10994-006-6226-1","type":"journal-article","created":{"date-parts":[[2006,3,7]],"date-time":"2006-03-07T12:56:50Z","timestamp":1141736210000},"page":"3-42","source":"Crossref","is-referenced-by-count":7071,"title":["Extremely randomized trees"],"prefix":"10.1007","volume":"63","author":[{"given":"Pierre","family":"Geurts","sequence":"first","affiliation":[]},{"given":"Damien","family":"Ernst","sequence":"additional","affiliation":[]},{"given":"Louis","family":"Wehenkel","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2006,3,2]]},"reference":[{"key":"6226_CR1","unstructured":"Ali, K., & Pazzani, M. (1996). Error reduction through learning multiple descriptions. Machine Learning, 24:3, 173--206."},{"key":"6226_CR2","unstructured":"Ali, K. (1995). On the link between error correlation and error reduction in decision tree ensembles. Technical report, Department of Information and Computer Science, University of California, Irvine."},{"key":"6226_CR3","doi-asserted-by":"crossref","unstructured":"Bauer, E., & Kohavi., R. (1999). An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Machine Learning, 36, 105--139.","DOI":"10.1023\/A:1007515423169"},{"key":"6226_CR4","unstructured":"Blake, C., & Merz, C.(1998). UCI repository of machine learning databases. http:\/\/www.ics.uci.edu\/~mlearn\/MLRepository.html."},{"key":"6226_CR5","unstructured":"Breiman, L., Friedman, J., Olsen, R., & Stone, C. (1984). Classification and regression trees. Wadsworth International."},{"key":"6226_CR6","unstructured":"Breiman, L. (1996a). Arcing classifiers. Technical report, University of California, Department of Statistics."},{"key":"6226_CR7","doi-asserted-by":"crossref","unstructured":"Breiman, L. (1996b). Bagging predictors. Machine Learning, 24:2, 123--140.","DOI":"10.1007\/BF00058655"},{"key":"6226_CR8","doi-asserted-by":"crossref","unstructured":"Breiman, L. (2000a). Randomizing outputs to increase prediction accuracy. Machine Learning, 40:3, 229--242.","DOI":"10.1023\/A:1007682208299"},{"key":"6226_CR9","unstructured":"Breiman, L. (2000b). Some infinity theory for predictor ensembles. Technical Report 579, University of California, Department of Statistics."},{"key":"6226_CR10","unstructured":"Breiman, L. (2001). Random forests. Machine Learning, 45, 5--32."},{"key":"6226_CR11","doi-asserted-by":"crossref","unstructured":"Buntine, W., & Niblett, T. (1992), A further comparison of splitting rules for decision-tree induction. Machine Learning, 8, 75--85.","DOI":"10.1007\/BF00994006"},{"key":"6226_CR12","unstructured":"Buntine, W., & Weigend, A. (1991). Bayesian back-propagation. Complex Systems, 5, 603--643."},{"key":"6226_CR13","doi-asserted-by":"crossref","unstructured":"Buntine, W. (1992). Learning classification trees. Statistics and Computing, 2, 63--73.","DOI":"10.1007\/BF01889584"},{"key":"6226_CR14","unstructured":"Cutler, A.,& Guohua, Z. (2001), PERT \u2014 Perfect random tree ensembles. Computing Science and Statistics 33."},{"key":"6226_CR15","unstructured":"Dietterich, T., & Kong, E. (1995). Machine learning bias, statistical bias, and statistical variance of decision tree algorithms. Technical report, Department of Computer Science, Oregon State University."},{"key":"6226_CR16","unstructured":"Dietterich, T. (2000). An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Machine Learning, 40:2, 139--157."},{"key":"6226_CR17","unstructured":"Ernst, D., Geurts, P., & Wehenkel, L. (2005). Tree-based batch mode reinforcement learning. Journal of Machine Learning Research, 6, 503--556."},{"key":"6226_CR18","unstructured":"Freund, Y., & Schapire, R. (1995). A decision-theoretic generalization of on-line learning and an application to boosting. In: Proceedings of the 2nd European Conference on Computational Learning Theory, 23--27."},{"key":"6226_CR19","unstructured":"Friedman, J. (1991). Multivariate adaptive regression splines. Annals of Statistics, 19:1, 1--141."},{"key":"6226_CR20","doi-asserted-by":"crossref","unstructured":"Friedman, J. (1997). On bias, variance, 0\/1-loss, and the curse-of-dimensionality. Data Mining and Knowledge Discovery, 1, 55--77.","DOI":"10.1023\/A:1009778005914"},{"key":"6226_CR21","doi-asserted-by":"crossref","unstructured":"Geman, S., Bienenstock, E., & Doursat, R. (1992). Neural networks and the bias\/variance dilemna. Neural Computation, 4, 1--58.","DOI":"10.1162\/neco.1992.4.1.1"},{"key":"6226_CR22","doi-asserted-by":"crossref","unstructured":"Geurts, P., Blanco Cuesta A., & Wehenkel, L. (2005a). Segment and combine approach for biological sequence classification. In: Proceedings of IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 194\u2013201.","DOI":"10.1109\/CIBCB.2005.1594917"},{"key":"6226_CR23","doi-asserted-by":"crossref","unstructured":"Geurts, P., Fillet,M., de Seny, D., Meuwis, M. -A., Merville, M. -P., & Wehenkel, L. (2005b). Proteomic mass spectra classification using decision tree based ensemble methods. Bioinformatics, 21:14, 3138\u2013 3145.","DOI":"10.1093\/bioinformatics\/bti494"},{"key":"6226_CR24","unstructured":"Geurts, P., & L. Wehenkel. (2000). Investigation and reduction of discretization variance in decision tree induction. In: Proceedings of the 11th European Conference on Machine Learning, 162--170."},{"key":"6226_CR25","unstructured":"Geurts, P., & Wehenkel, L. (2005). Segment and combine approach for non-parametric time-series classification. In: Proceedings of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases. pp. 478--485."},{"key":"6226_CR26","unstructured":"Geurts, P. (2002). Contributions to decision tree induction: bias\/variance tradeo. and time series classification. Ph.D. thesis, University of Li\u00e8ge."},{"key":"6226_CR27","unstructured":"Geurts, P. (2003). Extremely randomized trees. Technical report, University of Li\u00e8ge - Department of Electrical Engineering and Computer Science."},{"key":"6226_CR28","unstructured":"Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning: Data mining, inference, and prediction. Springer."},{"key":"6226_CR29","unstructured":"Herbrich, R., Graepel, T., & Campbell, C. (2001). Bayes point machines. Journal of Machine Learning Research, 1, 241--279."},{"key":"6226_CR30","unstructured":"Ho, T. (1998). The Random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20:8, 832--844."},{"key":"6226_CR31","doi-asserted-by":"crossref","unstructured":"James, G. (2003). Variance and bias for generalized loss functions. Machine Learning, 51, 115--135.","DOI":"10.1023\/A:1022899518027"},{"key":"6226_CR32","unstructured":"Kamath, C., Cantu-Paz, E., & Littau, D. (2002). Approximate splitting for ensembles of trees using histograms. In: Proceedings of the 2nd SIAM International Conference on Data mining."},{"key":"6226_CR33","doi-asserted-by":"crossref","unstructured":"Kleinberg, E. (1990). Stochastic discrimination. Annals of Mathematics and Artificial Intelligence 1, 207--239.","DOI":"10.1007\/BF01531079"},{"key":"6226_CR34","unstructured":"Lin, Y., & Jeon, Y. (2002). Random forests and adaptive nearest neighbors. Technical Report 1055, University of Wisconsin, Department of Statistics."},{"key":"6226_CR35","unstructured":"Mar\u00e9e, R., Geurts, P., Piater, J., & Wehenkel, L. (2004). A generic approach for image classsification based on decision tree ensembles and local sub-windows. In: Proceedings of the 6th Asian Conference on Computer Vision, 2, 860\u2013865."},{"key":"6226_CR36","doi-asserted-by":"crossref","unstructured":"Mingers, J. (1989). An empirical comparison of selection measures for decision-tree induction. Machine Learning, 3, 319--342.","DOI":"10.1007\/BF00116837"},{"key":"6226_CR37","unstructured":"Nadeau, C., & Bengio, Y. (2003). Inference for the generalization error. Machine Learning, 52:3, 239--281."},{"key":"6226_CR38","unstructured":"Quinlan, J. (1986). C4.5: Programs for machine learning. Morgan Kaufmann (San Mateo)."},{"key":"6226_CR39","unstructured":"Torgo, L. (1999). Inductive learning of tree-based regression models. Ph.D. thesis, University of Porto."},{"key":"6226_CR40","unstructured":"Valentini, G., & Dietterich, T. (2004). Bias-variance analysis of support vector machines for the development of SVM-based ensemble methods. Journal of Machine Learning Research, 5, 725--775."},{"key":"6226_CR41","unstructured":"Webb, G., & Zheng, Z. (2004). Multi-strategy ensemble learning: reducing error by combining ensemble learning techniques. IEEE Transactions on Knowledge and Data Engineering, 16:8, 980--991."},{"key":"6226_CR42","unstructured":"Webb, G. (2000). Multiboosting: a technique for combining boosting and wagging. Machine Learning, 40:2, 159--196."},{"key":"6226_CR43","unstructured":"Wehenkel, L., & Pavella, M. (1991). Decision trees and transient stability of electric power systems. Automatica, 27:1, 115--134."},{"key":"6226_CR44","unstructured":"Wehenkel, L. (1996). On uncertainty measures used for decision tree induction. In: Proceedings of Information Processing and Management of Uncertainty in Knowledge Based Systems, 413--418."},{"key":"6226_CR45","unstructured":"Wehenkel, L. (1997). Discretization of continuous attributes for supervised learning: variance evaluation and variance reduction. In: Proceedings of the International Fuzzy Systems Association World Congress, 381--388."},{"key":"6226_CR46","doi-asserted-by":"crossref","unstructured":"Wehenkel, L. (1998). Automatic Learning Techniques in Power Systems. Boston: Kluwer Academic.","DOI":"10.1007\/978-1-4615-5451-6"},{"key":"6226_CR47","doi-asserted-by":"crossref","unstructured":"Wolpert, D. (1992). Stacked generalization. Neural Networks, 5, 241--259.","DOI":"10.1016\/S0893-6080(05)80023-1"},{"key":"6226_CR48","unstructured":"Zhao, G. (2000). A new perspective on classification. Ph.D. thesis, Utah State University, Department of Mathematics and Statistics."},{"key":"6226_CR49","unstructured":"Zheng, Z., & Webb, G. (1998). Stochastic attribute selection committees. In: Proceedings of the 11h Australian Joint Conference on Artificial Intelligence, 321--332."}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-006-6226-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10994-006-6226-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-006-6226-1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,6,1]],"date-time":"2019-06-01T01:40:20Z","timestamp":1559353220000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10994-006-6226-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2006,3,2]]},"references-count":49,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2006,4]]}},"alternative-id":["6226"],"URL":"https:\/\/doi.org\/10.1007\/s10994-006-6226-1","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2006,3,2]]}}}