{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:34:38Z","timestamp":1778603678467,"version":"3.51.4"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2014,9,17]],"date-time":"2014-09-17T00:00:00Z","timestamp":1410912000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2015,11]]},"DOI":"10.1007\/s10618-014-0383-9","type":"journal-article","created":{"date-parts":[[2014,9,17]],"date-time":"2014-09-17T18:14:25Z","timestamp":1410977665000},"page":"1531-1559","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Ensemble methods for uplift modeling"],"prefix":"10.1007","volume":"29","author":[{"given":"Micha\u0142","family":"So\u0142tys","sequence":"first","affiliation":[]},{"given":"Szymon","family":"Jaroszewicz","sequence":"additional","affiliation":[]},{"given":"Piotr","family":"Rzepakowski","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2014,9,17]]},"reference":[{"key":"383_CR1","doi-asserted-by":"crossref","unstructured":"Abe N, Verma N, Apte C, Schroko R (2004) Cross channel optimized marketing by reinforcement learning. In: Proceedings of the tenth ACM SIGKDD conference on knowledge discovery and data mining (KDD\u201904), pp 767\u2013772","DOI":"10.1145\/1014052.1016912"},{"key":"383_CR2","unstructured":"Adomavicius G, Tuzhilin A (1997) Discovery of actionable patterns in databases: the action hierarchy approach. In: Proceedings of the third international conference on knowledge discovery and data mining (KDD\u201997), pp 111\u2013114"},{"issue":"3","key":"383_CR3","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1023\/A:1011429418057","volume":"5","author":"S Bay","year":"2001","unstructured":"Bay S, Pazzani M (2001) Detecting group differences: mining contrast sets. Data Min Knowl Discov 5(3):213\u2013246","journal-title":"Data Min Knowl Discov"},{"issue":"2","key":"383_CR4","first-page":"123","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":"383_CR5","doi-asserted-by":"crossref","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":"383_CR6","unstructured":"Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth, Belmont"},{"issue":"2","key":"383_CR7","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/BF01889584","volume":"2","author":"W Buntine","year":"1992","unstructured":"Buntine W (1992) Learning classification trees. Stat Comput 2(2):63\u201373","journal-title":"Stat Comput"},{"key":"383_CR8","unstructured":"Buttrey SE, Kobayashi I (2003) On strength and correlation in random forests. In: Proceedings of the joint statistical meetings. Section on statistical computing, San Francisco"},{"key":"383_CR9","unstructured":"Chickering DM, Heckerman D (2000) A decision theoretic approach to targeted advertising. In: Proceedings of the 16th conference in uncertainty in artificial intelligence (UAI\u201900). Stanford, pp 82\u201388"},{"issue":"4","key":"383_CR10","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1561\/0100000004","volume":"1","author":"I Csiszar","year":"2004","unstructured":"Csiszar I, Shields P (2004) Information theory and statistics: a tutorial. Found Trends Commun Inf Theory 1(4):417\u2013528","journal-title":"Found Trends Commun Inf Theory"},{"key":"383_CR11","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"},{"key":"383_CR12","doi-asserted-by":"crossref","unstructured":"Dietterich T (2000) Ensemble methods in machine learning. In: First international workshop on multiple classifier systems, pp. 1\u201315","DOI":"10.1007\/3-540-45014-9_1"},{"key":"383_CR13","doi-asserted-by":"crossref","unstructured":"Fan W, Wang H, Yu PS, Ma Sheng S (2003) Is random model better? On its accuracy and efficiency. In: Proceedings of the third IEEE international conference on data mining (ICDM\u201903), pp 51\u201359","DOI":"10.1109\/ICDM.2003.1250902"},{"issue":"1","key":"383_CR14","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund Y, Schapire RE (1997) A decision\u2013theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119\u2013139","journal-title":"J Comput Syst Sci"},{"issue":"1","key":"383_CR15","doi-asserted-by":"crossref","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. Mach Learn 63(1):3\u201342","journal-title":"Mach Learn"},{"key":"383_CR16","unstructured":"Grundhoefer MD (2009) Raising the bar in cross-sell marketing with uplift modeling. In: Predictive analytics world conference"},{"key":"383_CR17","doi-asserted-by":"crossref","unstructured":"Guelman L, Guill\u00e9n M, P\u00e9rez-Mar\u00edn AM (2012) Random forests for uplift modeling: an insurance customer retention case. In: Modeling and simulation in engineering, economics and management. Lecture notes in business information processing (LNBIP), vol 115. Springer, Berlin, pp 123\u2013133","DOI":"10.1007\/978-3-642-30433-0_13"},{"key":"383_CR18","doi-asserted-by":"crossref","unstructured":"Hansen LK, Salamon P (October 1990) Neural network ensembles. IEEE Trans Pattern Anal Mach Intell 12(10):993\u20131001","DOI":"10.1109\/34.58871"},{"issue":"3","key":"383_CR19","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1002\/dir.10035","volume":"16","author":"B Hansotia","year":"2002","unstructured":"Hansotia B, Rukstales B (2002) Incremental value modeling. J Interact Mark 16(3):35\u201346","journal-title":"J Interact Mark"},{"key":"383_CR20","unstructured":"Hillstrom K (2008) The MineThatData e-mail analytics and data mining challenge. MineThatData blog, http:\/\/blog.minethatdata.com\/2008\/03\/minethatdata-e-mail-analytics-and-data.html . Accessed 2 April 2012"},{"key":"383_CR21","doi-asserted-by":"crossref","unstructured":"Holland PW (December 1986) Statistics and causal inference. J Am Stat Assoc 81(396):945\u2013960","DOI":"10.1080\/01621459.1986.10478354"},{"key":"383_CR22","unstructured":"Ja\u015bkowski M, Jaroszewicz S (2012) Uplift modeling for clinical trial data. In: ICML, 2012 workshop on machine learning for clinical data analysis. Edinburgh, Scotland, June 2012"},{"key":"383_CR23","doi-asserted-by":"crossref","unstructured":"Kohavi R, Longbotham R, Sommerfield D, Henne RM (February 2009) Controlled experiments on the web: survey and practical guide. Data Min Knowl Discov 18(1):140\u2013181","DOI":"10.1007\/s10618-008-0114-1"},{"key":"383_CR24","unstructured":"Larsen K (2011) Net lift models: optimizing the impact of your marketing. In: Predictive analytics world, 2011. Workshop presentation"},{"issue":"1","key":"383_CR25","first-page":"355","volume":"32","author":"FT Liu","year":"2008","unstructured":"Liu FT, Ting KM, Yu Y, Zhou Z-H (2008) Spectrum of variable-random trees. J Artif Intell Res 32(1):355\u2013384","journal-title":"J Artif Intell Res"},{"issue":"2","key":"383_CR26","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1145\/772862.772872","volume":"4","author":"VSY Lo","year":"2002","unstructured":"Lo VSY (2002) The true lift model: a novel data mining approach to response modeling in database marketing. SIGKDD Explor 4(2):78\u201386","journal-title":"SIGKDD Explor"},{"key":"383_CR27","doi-asserted-by":"crossref","unstructured":"Pechyony D, Jones R, Li X (2013) A joint optimization of incrementality and revenue to satisfy both advertiser and publisher. In WWW 2013 Companion Publication, pp 123\u2013124","DOI":"10.1145\/2487788.2487846"},{"key":"383_CR28","doi-asserted-by":"crossref","DOI":"10.1002\/9780470870709","volume-title":"Competing risks: a practical perspective","author":"M Pintilie","year":"2006","unstructured":"Pintilie M (2006) Competing risks: a practical perspective. Wiley, Hoboken"},{"key":"383_CR29","volume-title":"C4.5: programs for machine learning","author":"J Quinlan","year":"1992","unstructured":"Quinlan J (1992) C4.5: programs for machine learning. Morgan Kauffman, Ann Arbor"},{"key":"383_CR30","unstructured":"Radcliffe N, Simpson R (April 2008) Identifying who can be saved and who will be driven away by retention activity. J Telecommun Manag 1(2):168"},{"key":"383_CR31","unstructured":"Radcliffe NJ, Surry PD (1999) Differential response analysis: modeling true response by isolating the effect of a single action. In: Proceedings of credit scoring and credit control VI. Credit Research Centre, University of Edinburgh Management School"},{"key":"383_CR32","unstructured":"Radcliffe NJ, Surry PD (2011) Real-world uplift modelling with significance-based uplift trees. Portrait Technical Report TR-2011-1, stochastic solutions"},{"key":"383_CR33","doi-asserted-by":"crossref","unstructured":"Ra\u015b Z, Wyrzykowska E, Tsay L-S (2009) Action rules mining. In: Encyclopedia of data warehousing and mining, vol 1. IGI Global, pp 1\u20135","DOI":"10.4018\/978-1-60566-010-3.ch001"},{"issue":"8","key":"383_CR34","doi-asserted-by":"crossref","first-page":"2379","DOI":"10.1080\/03610929408831393","volume":"23","author":"J Robins","year":"1994","unstructured":"Robins J (1994) Correcting for non-compliance in randomized trials using structural nested mean models. Commun Stat Theory Methods 23(8):2379\u20132412","journal-title":"Commun Stat Theory Methods"},{"issue":"4","key":"383_CR35","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1093\/biomet\/91.4.763","volume":"91","author":"J Robins","year":"2004","unstructured":"Robins J, Rotnitzky A (2004) Estimation of treatment effects in randomised trials with non-compliance and a dichotomous outcome using structural mean models. Biometrika 91(4):763\u2013783","journal-title":"Biometrika"},{"key":"383_CR36","doi-asserted-by":"crossref","unstructured":"Rzepakowski P, Jaroszewicz S (2010) Decision trees for uplift modeling. In: Proceedings of the 10th IEEE international conference on data mining (ICDM). Sydney, Australia, pp 441\u2013450","DOI":"10.1109\/ICDM.2010.62"},{"key":"383_CR37","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s10115-011-0434-0","volume":"32","author":"P Rzepakowski","year":"2012","unstructured":"Rzepakowski P, Jaroszewicz S (2012) Decision trees for uplift modeling with single and multiple treatments. Knowl Inf Syst 32:303\u2013327","journal-title":"Knowl Inf Syst"},{"key":"383_CR38","unstructured":"Segal MR (2004) Machine learning benchmarks and random forest regression. Technical report, Center for Bioinformatics & Molecular Biostatistics, University of California, San Francisco"},{"issue":"4","key":"383_CR39","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1046\/j.1369-7412.2003.00417.x","volume":"65","author":"S Vansteelandt","year":"2003","unstructured":"Vansteelandt S, Goetghebeur E (2003) Causal inference with generalized structural mean models. J R Stat Soc B 65(4):817\u2013835","journal-title":"J R Stat Soc B"},{"key":"383_CR40","volume-title":"Data mining: practical machine learning tools and techniques","author":"IH Witten","year":"2005","unstructured":"Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, Ann Arbor"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-014-0383-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10618-014-0383-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-014-0383-9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,15]],"date-time":"2019-08-15T03:42:20Z","timestamp":1565840540000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10618-014-0383-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,9,17]]},"references-count":40,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2015,11]]}},"alternative-id":["383"],"URL":"https:\/\/doi.org\/10.1007\/s10618-014-0383-9","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"value":"1384-5810","type":"print"},{"value":"1573-756X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,9,17]]}}}