{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T12:11:07Z","timestamp":1767183067296,"version":"3.40.3"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319712727"},{"type":"electronic","value":"9783319712734"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-3-319-71273-4_9","type":"book-chapter","created":{"date-parts":[[2017,12,29]],"date-time":"2017-12-29T09:12:26Z","timestamp":1514538746000},"page":"102-113","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Generalising Random Forest Parameter Optimisation to Include Stability and Cost"],"prefix":"10.1007","author":[{"given":"C. H. Bryan","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benjamin Paul","family":"Chamberlain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Duncan A.","family":"Little","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u00c2ngelo","family":"Cardoso","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,12,30]]},"reference":[{"issue":"2","key":"9_CR1","first-page":"123","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123\u2013140 (1996)","journal-title":"Mach. Learn."},{"issue":"6","key":"9_CR2","doi-asserted-by":"publisher","first-page":"2350","DOI":"10.1214\/aos\/1032181158","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman, L.: Heuristics of instability in model selection. Ann. Stat. 24(6), 2350\u20132383 (1996)","journal-title":"Ann. Stat."},{"key":"9_CR3","doi-asserted-by":"crossref","unstructured":"Chamberlain, B.P., Cardoso, A., Liu, C.H.B., Pagliari, R., Deisenroth, M.P.: Customer lifetime value prediction using embeddings. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1753\u20131762 (2017)","DOI":"10.1145\/3097983.3098123"},{"issue":"2\u20133","key":"9_CR4","first-page":"81","volume":"7","author":"A Criminisi","year":"2012","unstructured":"Criminisi, A.: Decision forests: a unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Found. Trends\u00ae Comput. Graph. Vis. 7(2\u20133), 81\u2013227 (2012)","journal-title":"Found. Trends\u00ae Comput. Graph. Vis."},{"issue":"1","key":"9_CR5","first-page":"55","volume":"6","author":"A Elisseeff","year":"2005","unstructured":"Elisseeff, A., Evgeniou, T., Pontil, M.: Stability of randomized learning algorithms. J. Mach. Learn. Res. 6(1), 55\u201379 (2005)","journal-title":"J. Mach. Learn. Res."},{"key":"9_CR6","first-page":"3133","volume":"15","author":"M Fern\u00e1ndez-Delgado","year":"2014","unstructured":"Fern\u00e1ndez-Delgado, M., Cernadas, E., Barro, S., Amorim, D., Amorim Fern\u00e1ndez-Delgado, D.: Do we need hundreds of classifiers to solve real world classification problems? J. Mach. Learn. Res. 15, 3133\u20133181 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"9_CR7","unstructured":"Hoffman, M.W., Shahriari, R.: Modular mechanisms for Bayesian optimization. In: NIPS Workshop on Bayesian Optimization (2014)"},{"issue":"1","key":"9_CR8","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1186\/s12859-016-1228-x","volume":"17","author":"BFF Huang","year":"2016","unstructured":"Huang, B.F.F., Boutros, P.C.: The parameter sensitivity of random forests. BMC Bioinform. 17(1), 331 (2016)","journal-title":"BMC Bioinform."},{"issue":"1","key":"9_CR9","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1186\/1472-6947-11-51","volume":"11","author":"M Khalilia","year":"2011","unstructured":"Khalilia, M., Chakraborty, S., Popescu, M.: Predicting disease risks from highly imbalanced data using random forest. BMC Med. Inf. Dec. Making 11(1), 51 (2011)","journal-title":"BMC Med. Inf. Dec. Making"},{"issue":"1","key":"9_CR10","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1115\/1.3653121","volume":"86","author":"HJ Kushner","year":"1964","unstructured":"Kushner, H.J.: A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise. J. Basic Eng. 86(1), 97\u2013106 (1964)","journal-title":"J. Basic Eng."},{"key":"9_CR11","first-page":"3735","volume":"15","author":"R Martinez-Cantin","year":"2014","unstructured":"Martinez-Cantin, R.: BayesOpt: a bayesian optimization library for nonlinear optimization, experimental design and bandits. J. Mach. Learn. Res. 15, 3735\u20133739 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"9_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1007\/3-540-07165-2_55","volume-title":"Optimization Techniques IFIP Technical Conference Novosibirsk, July 1\u20137, 1974","author":"J Mo\u010dkus","year":"1975","unstructured":"Mo\u010dkus, J.: On bayesian methods for seeking the extremum. In: Marchuk, G.I. (ed.) Optimization Techniques 1974. LNCS, vol. 27, pp. 400\u2013404. Springer, Heidelberg (1975). https:\/\/doi.org\/10.1007\/3-540-07165-2_55"},{"key":"9_CR13","unstructured":"Snoek, J., Larochelle, H., Adams, R.: Practical bayesian optimization of machine learning algorithms. In: Advances in Neural Information Processing Systems, pp. 2951\u20132959 (2012)"},{"issue":"2","key":"9_CR14","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1177\/1094670515616376","volume":"19","author":"A Tamaddoni","year":"2016","unstructured":"Tamaddoni, A., Stakhovych, S., Ewing, M.: Comparing churn prediction techniques and assessing their performance: a contingent perspective. J. Serv. Res. 19(2), 123\u2013141 (2016)","journal-title":"J. Serv. Res."},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"Vanderveld, A., Pandey, A., Han, A., Parekh, R.: An engagement-based customer lifetime value system for e-commerce. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 293\u2013302 (2016)","DOI":"10.1145\/2939672.2939693"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-71273-4_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T01:17:52Z","timestamp":1672276672000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-71273-4_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319712727","9783319712734"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-71273-4_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2017]]},"assertion":[{"value":"30 December 2017","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Skopje","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Macedonia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2017","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2017","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2017","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2017","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ecmlpkdd2017.ijs.si\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}