Amemiya T (1973) Regression analysis when the dependent variable is truncated normal. Econometrica 41(6):997–1016
Ayer M, Brunk H, Ewing G, Reid W, Silverman E (1955) An empirical distribution function for sampling with incomplete information. Ann Math Stat 5:641–647
Bella A, Ferri C, Hernandez-Orallo J, Ramirez-Quintana M (2009) Calibration of machine learning models. In: Handbook of research on machine learning applications. IGI Global, Hershey, pp 128–146
[+]
Amemiya T (1973) Regression analysis when the dependent variable is truncated normal. Econometrica 41(6):997–1016
Ayer M, Brunk H, Ewing G, Reid W, Silverman E (1955) An empirical distribution function for sampling with incomplete information. Ann Math Stat 5:641–647
Bella A, Ferri C, Hernandez-Orallo J, Ramirez-Quintana M (2009) Calibration of machine learning models. In: Handbook of research on machine learning applications. IGI Global, Hershey, pp 128–146
Bella A, Ferri C, Hernández-Orallo J, Ramírez-Quintana M (2009) Similarity-binning averaging: a generalisation of binning calibration. In: Intelligent data engineering and automated learning—IDEAL 2009. Lecture notes in computer science, vol 5788. Springer, Berlin/Heidelberg, pp 341–349
Bennett PN (2006) Building reliable metaclassifiers for text learning. PhD thesis, Carnegie Mellon University
Bennett PN, Dumais ST, Horvitz E (2005) The combination of text classifiers using reliability indicators. Inf Retr 8(1):67–98
Blake C, Merz C (1998) UCI repository of machine learning databases. http://www.ics.uci.edu/~mlearn/MLRepository.html
Breiman L (1996) Bagging predictors. Mach Learn 24:123–140
Brier G (1950) Verification of forecasts expressed in terms of probabilities. Mon Weather Rev 78:1–3
Brümmer N (2010) Measuring, refining and calibrating speaker and language information extracted from speech. PhD thesis, University of Stellenbosch
Canuto A, Santos A, Vargas R (2011) Ensembles of artmap-based neural networks: an experimental study. Appl Intell 35:1–17
Caruana R, Munson A, Mizil AN (2006) Getting the most out of ensemble selection. In: ICDM ’06: proceedings of the sixth international conference on data mining. IEEE Computer Society, Washington, pp 828–833
Caruana R, Niculescu-Mizil A (2004) Data mining in metric space: an empirical analysis of supervised learning performance criteria. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’04. ACM Press, New York, pp 69–78
Cohen I, Goldszmidt M (2004) Properties and benefits of calibrated classifiers. In: Proceedings of the 8th European conference on principles and practice of knowledge discovery in databases, PKDD ’04. Springer, Berlin, pp 125–136
Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30
Dietterich TG (2000) Ensemble methods in machine learning. In: Proceedings of the first international workshop on multiple classifier systems, MCS ’00. Springer, London, pp 1–15
Dietterich TG (2000) An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach Learn 40:139–157
Fahim M, Fatima I, Lee S, Lee Y (2012) Eem: evolutionary ensembles model for activity recognition in smart homes. Appl Intell, 1–11. doi: 10.1007/s10489-012-0359-7
Ferri C, Flach P, Hernández-Orallo J (2004) Delegating classifiers. In: Proceedings of the twenty-first international conference on machine learning, ICML ’04. ACM Press, New York, pp 37–45
Ferri C, Hernández-Orallo J, Modroiu R (2009) An experimental comparison of performance measures for classification. Pattern Recognit Lett 30:27–38
Ferri C, Hernández-Orallo J, Salido M (2003) Volume under the ROC surface for multi-class problems. Exact computation and evaluation of approximations. In: Proceedings of 14th European conference on machine learning, pp 108–120
Flach P, Blockeel H, Ferri C, Hernández-Orallo J, Struyf J (2003) Decision support for data mining: an introduction to ROC analysis and its applications. In: Data mining and decision support: integration and collaboration. Kluwer Academic, Boston, pp 81–90
Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: International conference on machine learning, pp 148–156
Gama J, Brazdil P (2000) Cascade generalization. Mach Learn 41:315–343
Garczarek U (2002) Classification rules in standardized partition spaces. PhD thesis, Universitat Dortmund
Gebel M (2009) Multivariate calibration of classifier scores into the probability space. PhD thesis, University of Dortmund
Hand DJ, Till RJ (2001) A simple generalisation of the area under the ROC curve for multiple class classification problems. Mach Learn 45:171–186
Hoeting JA, Madigan D, Raftery AE, Volinsky CT (1999) Bayesian model averaging: a tutorial. Stat Sci 14(4):382–417
Khor K, Ting C, Phon-Amnuaisuk S (2012) A cascaded classifier approach for improving detection rates on rare attack categories in network intrusion detection. Appl Intell 36:320–329
Kuncheva LI (2002) A theoretical study on six classifier fusion strategies. IEEE Trans Pattern Anal Mach Intell 24:281–286
Kuncheva LI (2004) Combining pattern classifiers: methods and algorithms. Wiley-Interscience, New York
Kuncheva LI (2005) Diversity in multiple classifier systems. Inf Fusion 6(1):3–4
Kuncheva LI, Whitaker CJ (2003) Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach Learn 51:181–207
Lee H, Kim E, Pedrycz W (2012) A new selective neural network ensemble with negative correlation. Appl Intell, 1–11. doi: 10.1007/s10489-012-0342-3
Maudes J, Rodríguez J, García-Osorio C, Pardo C (2011) Random projections for linear svm ensembles. Appl Intell 34:347–359
Murphy AH (1972) Scalar and vector partitions of the probability score: part II. n-State situation. J Appl Meteorol 11:1182–1192
Platt JC (1999) Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in large margin classifiers. MIT Press, Boston, pp 61–74
Raftery AE, Gneiting T, Balabdaoui F, Polakowski M (2005) Using Bayesian model averaging to calibrate forecast ensembles. Monthly Weather Rev, p 133
Rifkin R, Klautau A (2004) In defense of one-vs-all classification. J Mach Learn Res 5:101–141
Robertson T, Wright FT, Dykstra RL (1988) Order restricted statistical inference. Wiley, New York
Souza L, Pozo A, Rosa J, Neto A (2010) Applying correlation to enhance boosting technique using genetic programming as base learner. Appl Intell 33:291–301
Tulyakov S, Jaeger S, Govindaraju V, Doermann D (2008) Review of classifier combination methods. In: Marinai HFS (ed) Studies in computational intelligence: machine learning in document analysis and recognition. Springer, Berlin, pp 361–386
Verma B, Hassan S (2011) Hybrid ensemble approach for classification. Appl Intell 34:258–278
Wang C, Hunter A (2010) A low variance error boosting algorithm. Appl Intell 33:357–369
Witten IH, Frank E (2002) Data mining: practical machine learning tools and techniques with java implementations. SIGMOD Rec 31:76–77
Wolpert DH (1992) Stacked generalization. Neural Netw 5:241–259
Zadrozny B, Elkan C (2002) Transforming classifier scores into accurate multiclass probability estimates. In: Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’02. ACM Press, New York, pp 694–699
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