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Knowl. Discov. Data"],"published-print":{"date-parts":[[2014,10,7]]},"abstract":"<jats:p>\n                    Common-day applications of predictive models usually involve the full use of the available contextual information. When the operating context changes, one may fine-tune the by-default (incontextual) prediction or may even abstain from predicting a value (a reject).\n                    <jats:italic>Global<\/jats:italic>\n                    reframing solutions, where the same function is applied to adapt the estimated outputs to a new cost context, are possible solutions here. An alternative approach, which has not been studied in a comprehensive way for regression in the knowledge discovery and data mining literature, is the use of a\n                    <jats:italic>local<\/jats:italic>\n                    (e.g., probabilistic) reframing approach, where decisions are made according to the estimated output\n                    <jats:italic>and<\/jats:italic>\n                    a reliability, confidence, or probability estimation. In this article, we advocate for a simple two-parameter (mean and variance) approach, working with a\n                    <jats:italic>normal<\/jats:italic>\n                    conditional probability density. Given the conditional mean produced by any regression technique, we develop lightweight \u201cenrichment\u201d methods that produce good estimates of the conditional variance, which are used by the\n                    <jats:italic>probabilistic<\/jats:italic>\n                    (local) reframing methods. We apply these methods to some very common families of cost-sensitive problems, such as optimal predictions in (auction) bids, asymmetric loss scenarios, and rejection rules.\n                  <\/jats:p>","DOI":"10.1145\/2641758","type":"journal-article","created":{"date-parts":[[2014,8,26]],"date-time":"2014-08-26T08:08:55Z","timestamp":1409040535000},"page":"1-55","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Probabilistic Reframing for Cost-Sensitive Regression"],"prefix":"10.1145","volume":"8","author":[{"given":"Jos\u00e9","family":"Hern\u00b4ndez-Orallo","sequence":"first","affiliation":[{"name":"Universitat Polit\u00e8cnica de Val\u00e8ncia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2014,8,25]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.2753\/MIS0742-1222250309"},{"key":"e_1_2_1_2_1","first-page":"1","article-title":"Bayesian approach to life testing and reliability estimation using asymmetric loss function","volume":"29","author":"Basu A. 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Herrera. 2008. An extension on statistical comparisons of classifiers over multiple data sets for all pairwise comparisons. The Journal of Machine Learning Research 9, 2677--2694 (2008), 66.","journal-title":"The Journal of Machine Learning Research"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/1081870.1081918"},{"key":"e_1_2_1_28_1","volume-title":"Prediction with a generalized cost of error function. Operational Research","author":"Granger C. W. J.","year":"1969","unstructured":"C. W. J. Granger . 1969. Prediction with a generalized cost of error function. Operational Research ( 1969 ), 199--207. C. W. J. Granger. 1969. Prediction with a generalized cost of error function. Operational Research (1969), 199--207."},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/s101080050007"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214504000000548"},{"key":"e_1_2_1_31_1","doi-asserted-by":"crossref","unstructured":"P. Hall R. 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Lecture Notes in Computer Science , Vol. 2430 . Springer, Berlin, 185--194. H. Papadopoulos, K. Proedrou, V. Vovk, and A. Gammerman. 2002. Inductive confidence machines for regression. In Machine Learning: ECML 2002, Tapio Elomaa, Heikki Mannila, and Hannu Toivonen (Eds.). Lecture Notes in Computer Science, Vol. 2430. Springer, Berlin, 185--194."},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.5555\/2016945.2016967"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-007-5013-y"},{"key":"e_1_2_1_46_1","volume-title":"Advances in Large Margin Classifiers","author":"Platt J. C.","unstructured":"J. C. Platt . 1999. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods . In Advances in Large Margin Classifiers . MIT Press , Boston , 61--74. J. C. Platt. 1999. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Advances in Large Margin Classifiers. MIT Press, Boston, 61--74."},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1024099825458"},{"key":"e_1_2_1_48_1","volume-title":"R: A language and environment for statistical computing","author":"Team R","year":"2012","unstructured":"R Team and others. 2012 . R: A language and environment for statistical computing . R Foundation for Statistical Computing , Vienna, Austria . R Team and others. 2012. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria."},{"key":"e_1_2_1_50_1","first-page":"31","article-title":"Conditional probability density and regression estimators","author":"Rosenblatt M.","year":"1969","unstructured":"M. Rosenblatt . 1969 . Conditional probability density and regression estimators . Multivariate Analysis II 25 (1969), 31 . M. Rosenblatt. 1969. Conditional probability density and regression estimators. 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