EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 245 (2020) EPJ Web Conf., 245 (2020) 06038 References
Open Access
Issue
EPJ Web Conf.
Volume 245, 2020
24th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
Article Number 06038
Number of page(s) 15
Section 6 - Physics Analysis
DOI https://doi.org/10.1051/epjconf/202024506038
Published online 16 November 2020
  1. A. Valassi, Binary classifier metrics for optimizing HEP event selection, Proc. CHEP2018, Sofia, EPJ Web of Conf. 214 (2019) 06004. https://doi.org/10.1051/epjconf/201921406004 [CrossRef] [Google Scholar]
  2. G. W. Brier, Verification of forecasts expressed in terms of probability, Weather Rev. 78 (1950) 1. https://doi.org/10.1175/1520-0493(1950)078%3C0001:VOFEIT%3E2.0.CO;2 [CrossRef] [Google Scholar]
  3. OPAL Collaboration, Measurement of the W boson mass and W+Wproduction and decay properties in e+ecollisions at √s=172 GeV, Eur. Phys. J. C 1 (1998) 395. https://doi.org/10.1007/s100520050093 [CrossRef] [EDP Sciences] [Google Scholar]
  4. ALEPH Collaboration, Measurement of the W mass by direct reconstruction in e+e− collisions at 172 GeV, Phys. Lett. B 422 (1998) 384. https://doi.org/10.1016/S0370-2693(98)00062-8 [Google Scholar]
  5. V. Lemaitre and ALEPH Collaboration, Single W Production at Energies up to √s=202 GeV and Search for Anomalous Triple Gauge Boson Couplings, Proc. 30th Int. Conf. on High-Energy Physics (ICHEP2000), Osaka (2000). http://cds.cern.ch/record/531207 [Google Scholar]
  6. J. S. Gainer, J. Lykken, K. T. Matchev, S. Mrenna, M. Park, Exploring theory space with Monte Carlo reweighting, JHEP 2014 (2014) 78. https://doi.org/10.1007/JHEP10(2014)078 [Google Scholar]
  7. O. Mattelaer, On the maximal use of Monte Carlo samples: re-weighting events at NLO accuracy, Eur. Phys. J. C 76 (2016) 674. https://doi.org/10.1140/epjc/s10052-016-4533-7 [CrossRef] [EDP Sciences] [Google Scholar]
  8. K. Kondo, Dynamical Likelihood Method for Reconstruction of Events with Missing Momentum. I. Method and Toy Models, J. Phys. Soc. Jpn. 57 (1988) 4126. https://doi.org/10.1143/JPSJ.57.4126 [Google Scholar]
  9. R. H. Dalitz, G. R. Goldstein, Decay and polarization properties of the top quark, Phys. Rev. D 45 (1992) 1531. https://doi.org/10.1103/PhysRevD.45.1531 [Google Scholar]
  10. D0 Collaboration, A precision measurement of the mass of the top quark, Nature 429 (2004) 638. https://doi.org/10.1038/nature02589 [CrossRef] [PubMed] [Google Scholar]
  11. K. Kondo, Dynamical Likelihood Method and Top Quark Mass Measurement at CDF, J. Phys. Conf. Series 53 (2006) 009. https://doi.org/10.1088/1742-6596/53/1/009 [CrossRef] [Google Scholar]
  12. CDF Collaboration, Precision measurement of the top-quark mass from dilepton events at CDF II, Phys. Rev. D 75 (2007) 031105(R). https://doi.org/10.1103/PhysRevD.75.031105 [Google Scholar]
  13. O. Mattelaer, P. Artoisenet, MadWeight: automatic event reweighting with matrix elements, Proc. CHARGED2008, Uppsala (2008). https://doi.org/10.22323/1.073.0025 [Google Scholar]
  14. A. van den Bos, Parameter Estimation for Scientists and Engineers Wiley (2007). [CrossRef] [Google Scholar]
  15. D. Gelé, T. G. Shears, W. J. Stirling, A. Valassi, M. F. Watson, Measurement of MW from the W+W− Threshold Cross-Section, Proc. Workshop on Physics at LEP2 vol.1, CERN96-01-V-1 (1996). https://doi.org/10.5170/CERN-1996-001-V-1 [Google Scholar]
  16. A. Valassi, Mesure de la masse du boson W au seuil, Doctoral thesis, Paris (1997). https://doi.org/10.17181/CERN.LT3V.WJKI [Google Scholar]
  17. P. Buschmann and DELPHI Collaboration, Measurement of the W-pair production cross-section and W branching ratios at √s=192-202 GeV, Proc. 30th Int. Conf. on High-Energy Physics (ICHEP2000), Osaka (2000). http://cds.cern.ch/record/2627765 [Google Scholar]
  18. ALEPH Collaboration, Measurement of the W mass in e+e− collisions at production threshold, Phys. Lett. B 401 (1997) 347. https://doi.org/10.1016/S0370-2693(97)00460-7 [Google Scholar]
  19. D0 Collaboration, Evidence for production of single top quarks, Phys. Rev. D 78 (2008) 012005. https://doi.org/10.1103/PhysRevD.78.012005 [Google Scholar]
  20. CDF Collaboration, Observation of Electroweak Single Top-Quark Production, Phys. Rev. Lett. 103 (2009) 092002. https://doi.org/10.1103/PhysRevLett.103.092002 [CrossRef] [PubMed] [Google Scholar]
  21. CMS Collaboration, Measurement of the t-Channel Single Top Quark Production Cross Section in pp Collisions at √s = 7 TeV, Phys. Rev. Lett. 107 (2011) 091802. https://doi.org/10.1103/PhysRevLett.107.091802 [CrossRef] [PubMed] [Google Scholar]
  22. D. Atwood, A. Soni, Analysis for magnetic moment and electric dipole moment form factors of the top quark via e+e− tt¯, Phys. Rev. D 45 (1992) 2405. https://doi.org/10.1103/PhysRevD.45.2405 [Google Scholar]
  23. M. Davier, L. Duflot, F. LeDiberder, A. Rougé, The optimal method for the measurement of tau polarization, Phys. Lett. B 306 (1993) 411. https://doi.org/10.1016/0370-2693(93)90101-M [Google Scholar]
  24. M. Diehl, O. Nachtmann, Optimal observables for the measurement of three-gauge-boson couplings in e+e− W+W−, Z. Phys. C 62 (1994) 397. https://doi.org/10.1007/BF01555899 [Google Scholar]
  25. O. Nachtmann, F. Nagel, Optimal observables and phase-space ambiguities, Eur. Phys. J. C 40 (2005) 497. https://doi.org/10.1140/epjc/s2005-02153-9 [CrossRef] [Google Scholar]
  26. L. Breiman, J. H. Friedman, R. A. Olshen, C. J. Stone, Classification And Regression Trees, Chapman and Hall (1984). https://doi.org/10.1201/9781315139470 [Google Scholar]
  27. R. Caruana, A. Niculescu-Mizil, Data mining in metric space: an empirical analysis of supervised learning performance criteria, Proc. 10th Int. Conf. on Knowledge Discovery and Data Mining (KDD-04), Seattle (2004). https://doi.org/10.1145/1014052.1014063 [Google Scholar]
  28. C. Ferri, J. Hernández-Orallo, R. Modroiu, An Experimental Comparison of Classification Performance Metrics, Proc. Learning 2004, Elche (2004). http://dmip.webs.upv.es/papers/Learning2004.pdf [Google Scholar]
  29. S. Wu, P. Flach, C. Ferri, An Improved Model Selection Heuristic for AUC, Proc. 18th Eur. Conf. Machine Learning (ECML 2007), Warsaw (2007). https://doi.org/10.1007/978-3-540-74958-5_44 [Google Scholar]
  30. C. Ferri, J. Hernández-Orallo, R. Modroiu, An Experimental Comparison of Performance Measures for Classification, Pattern Recognition Letters 30 (2009) 27. https://doi.org/10.1016/j.patrec.2008.08.010 [Google Scholar]
  31. W. W. Peterson, T. G. Birdsall, The theory of signal detectability (Part I: The general theory. Part II: Applications with Gaussian noise), Electronic Defense Group, Univ. of Michigan, Tech. Report No. 13 (1953). http://hdl.handle.net/2027.42/7068 [Google Scholar]
  32. W. P. Tanner, J. A. Swets, A decision-making theory of visual detection, Psychological Review 61 (1954) 401. https://doi.org/10.1037/h0058700 [CrossRef] [PubMed] [Google Scholar]
  33. W. W. Peterson, T. G. Birdsall, W. C. Fox, The theory of signal detectability, Transactions of the IRE Professional Group on Information Theory (PGIT) 4 (1954) 171. https://doi.org/10.1109/TIT.1954.1057460 [CrossRef] [Google Scholar]
  34. W. P. Tanner, J. A. Swets, The human use of information I: Signal detection for the case of the signal known exactly, Transactions of the IRE Professional Group on Information Theory (PGIT) 4 (1954) 213. https://doi.org/10.1109/TIT.1954.1057461 [CrossRef] [Google Scholar]
  35. D. van Meter, D. Middleton, Modern statistical approaches to reception in communication theory. Transactions of the IRE Professional Group on Information Theory (PGIT) 4 (1954) 119. https://doi.org/10.1109/TIT.1954.1057471 [CrossRef] [Google Scholar]
  36. J. A. Swets, W. P. Tanner, T. G. Birdsall, The evidence for a decision-making theory of visual detection, Electronic Defense Group, Univ. of Michigan, Tech. Report No. 40 (1955). http://hdl.handle.net/2027.42/7843 [Google Scholar]
  37. J. P. Egan, F. R. Clarke, E. C. Carterette, On the Transmission and Confirmation of Messages in Noise, J. Acoustical Soc. Am. 28 (1956) 536. https://doi.org/10.1121/1.1908387 [CrossRef] [Google Scholar]
  38. J. A. Swets, W. P. Tanner, T. G. Birdsall, Decision processes in perception, Psychological Review 68 (1961) 301. https://doi.org/10.1037/h0040547 [CrossRef] [PubMed] [Google Scholar]
  39. T. G. Birdsall, The theory of signal detectability : ROC curves and their character, Univ. of Michigan, Tech. Report No. 177 (1973). http://hdl.handle.net/2027.42/3618 [Google Scholar]
  40. L. B. Lusted, Logical Analysis in Roentgen Diagnosis, Radiology 74 (1960) 178. https://doi.org/10.1148/74.2.178 [PubMed] [Google Scholar]
  41. L. B. Lusted, Introduction to Medical Decision Making, Charles C. Thomas (1968). [Google Scholar]
  42. L. B. Lusted, Signal Detectability and Medical Decision-Making, Science 171 (1971) 1217. https://doi.org/10.1126/science.171.3977.1217 [Google Scholar]
  43. C. E. Metz, D. J. Goodenough, K. Rossmann, Evaluation of Receiver Operating Characteristic Curve Data in Terms of Information Theory, with Applications in Radiography, Radiology 109 (1973) 297. https://doi.org/10.1148/109.2.297 [PubMed] [Google Scholar]
  44. C. E. Metz, S. J. Starr, L. B. Lusted, K. Rossmann, Progress in evaluation of human observer visual detection performance using the ROC curve approach, Report CEA-CR-6, Proc. Int. Conf. on information processing in scintigraphy, Orsay (1975). https://inis.iaea.org/collection/NCLCollectionStore/_Public/07/248/7248574.pdf [Google Scholar]
  45. B. J. McNeil, E. Keeler, S. J. Adelstein, Primer on Certain Elements of Medical Decision Making, New England Journal of Medicine 293 (1975) 211. https://doi.org/10.1056/NEJM197507312930501 [CrossRef] [Google Scholar]
  46. C. E. Metz, Basic principles of ROC analysis, Seminars in Nuclear Medicine 8 (1978) 283. https://doi.org/10.1016/S0001-2998(78)80014-2 [CrossRef] [PubMed] [Google Scholar]
  47. L. B. Lusted, ROC Recollected, Medical Decision Making 4 (1984) 131. https://doi.org/10.1177/0272989X8400400201 [CrossRef] [Google Scholar]
  48. J. A. Swets, ROC Analysis Applied to the Evaluation of Medical Imaging Techniques, Inv. Radiology 14 (1979) 109. https://doi.org/10.1097/00004424-197903000-00002 [Google Scholar]
  49. J. A. Swets, Measuring the accuracy of diagnostic systems, Science 240 (1988) 1285. https://doi.org/10.1126/science.3287615 [Google Scholar]
  50. K. A. Spackman, Signal detection theory: valuable tools for evaluating inductive learning, Proc. 6th Int. Workshop on Machine Learning, Ithaca (1989). https://doi.org/10.1016/B978-1-55860-036-2.50047-3 [Google Scholar]
  51. A. P. Bradley, The use of the area under the ROC curve in the evaluation of Machine Learning algorithms, Pattern Recognition 30 (1997) 1145. https://doi.org/10.1016/S0031-3203(96)00142-2 [Google Scholar]
  52. F. J. Provost, T. Fawcett, Analysis and Visualization of Classifier Performance: Comparison Under Imprecise Class and Cost Distributions, Proc. KDD-97, Newport Beach (1997). https://aaai.org/Library/KDD/1997/kdd97-007.php [Google Scholar]
  53. F. J. Provost, T. Fawcett, R. Kohavi, The Case against Accuracy Estimation for Comparing Induction Algorithms, Proc. 15th Int. Conf. on Machine Learning (ICML ’98), Madison (1998). https://dl.acm.org/doi/abs/10.5555/645527.657469 [Google Scholar]
  54. T. Fawcett, Introduction to ROC analysis, Pattern Recognition Letters 27 (2006) 861. https://doi.org/10.1016/j.patrec.2005.10.010 [Google Scholar]
  55. A. Kent, M. M. Berry, F. U. Luehrs, J. W. Perry, Machine literature searching VIII Operational criteria for designing information retrieval systems, Amer. Doc. 6 (1955) 93. https://doi.org/10.1002/asi.5090060209 [CrossRef] [Google Scholar]
  56. C. W. Cleverdon, ASLIB Cranfield Research Project: report on the testing and analysis of an investigation into the comparative efficiency of indexing systems (1962). http://hdl.handle.net/1826/836 [Google Scholar]
  57. J. A. Swets, Information Retrieval Systems, Science 141 (1963) 245. https://doi.org/10.1126/science.141.3577.245 [Google Scholar]
  58. C. W. Cleverdon, The Cranfield Hypotheses, The Library Quarterly 35 (1965) 121. https://doi.org/10.1086/619319 [Google Scholar]
  59. C. J. van Rijsbergen, Foundation of evaluation, J. Documentation 30 (1974) 365. https://doi.org/10.1108/eb026584 [CrossRef] [Google Scholar]
  60. C. J. van Rijsbergen, Information retrieval, Butterworths (1979). http://www.dcs.glasgow.ac.uk/Keith/Preface.html [Google Scholar]
  61. C. D. Manning, P. Raghavan, H. Schütze, Introduction to Information Retrieval Cambridge University Press (2008). https://nlp.stanford.edu/IR-book [CrossRef] [Google Scholar]
  62. G. Punzi, Sensitivity of searches for new signals and its optimization, Proc. PhyStat2003, Stanford (2003). https://arxiv.org/abs/physics/0308063v2 [Google Scholar]
  63. R. D. Cousins, J. T. Linnemann, J. Tucker, Evaluation of three methods for calculating statistical significance when incorporating a systematic uncertainty into a test of the background-only hypothesis for a Poisson process, Nucl. Instr. Meth. Phys. Res. A 595 (2008) 480. https://doi.org/10.1016/j.nima.2008.07.086 [CrossRef] [Google Scholar]
  64. G. Cowan, K. Cranmer, E. Gross, O. Vitells, Asymptotic formulae for likelihood-based tests of new physics, Eur. Phys. J. C 71 (2011) 1554. https://doi.org/10.1140/epjc/s10052-011-1554-0 [CrossRef] [EDP Sciences] [Google Scholar]
  65. C. Adam-Bourdarios et al., The Higgs Machine Learning Challenge, Proc. NIPS 2014 Workshop on High-Energy Physics and Machine Learning (HEPML2014), Montreal (2014). https://hal.inria.fr/hal-01208587 [Google Scholar]
  66. M. Sokolova, G. Lapalme, A Systematic Analysis of Performance Measures for Classification Tasks, Information Processing and Management 45 (2009) 427. https://doi.org/10.1016/j.ipm.2009.03.002 [CrossRef] [Google Scholar]
  67. A. Luque, A Carrasco, A. Martin, J. R. Lama, Exploring Symmetry of Binary Classification Performance Metrics, Symmetry 11 (2019) 47. https://doi.org/10.3390/sym11010047. [Google Scholar]
  68. J. Tague-Sutcliffe, J. Blustein, A statistical analysis of the TREC-3 data, Overview of the Third Text REtrieval Conference (TREC-3), NIST Special Publication 500-226 (1995). https://trec.nist.gov/pubs/trec3/papers/T-SB.pdf [Google Scholar]
  69. D. Harman (editor), TREC-3 Results Appendix A: Evaluation Techniques and Measures, Overview of the Third Text REtrieval Conference (TREC-3), NIST Special Publication 500-226 (1995). https://trec.nist.gov/pubs/trec3/t3_proceedings.html [Google Scholar]
  70. D. Harman, Overview of the 2nd text retrieval conference (TREC-2), Information Processing and Management 31 (1995) 271. https://doi.org/10.1016/0306-4573(94)00047-7 [CrossRef] [Google Scholar]
  71. D. Hull, Using statistical testing in the evaluation of retrieval experiments, Proc. 16th ACM SIGIR Conf. (SIGIR 1993), Pittsburgh (1993). https://doi.org/10.1145/160688.160758 [Google Scholar]
  72. D. M. Green, General Prediction Relating Yes-No and Forced-Choice Results, J. Acoustical Soc. Am. 36 (1964) 1042. https://doi.org/10.1121/1.2143339 [CrossRef] [Google Scholar]
  73. D. M. Green, J. A. Swets, Signal detection theory and psychophysics, Wiley (1966). [Google Scholar]
  74. D. J. Goodenough, K. Rossmann, L. B. Lusted, Radiographic applications of signal detection theory, Radiology 105 (1972) 199. https://doi.org/10.1148/105.1.199 [PubMed] [Google Scholar]
  75. D. Bamber, The area above the ordinal dominance graph and the area below the receiver operating characteristic graph, J. Math. Psych. 12 (1975) 387. https://doi.org/10.1016/0022-2496(75)90001-2 [CrossRef] [Google Scholar]
  76. J. A. Hanley, B. J. McNeil, The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology 143 (1982) 29. https://doi.org/10.1148/radiology.143.1.7063747 [CrossRef] [PubMed] [Google Scholar]
  77. M. Greiner, D. Pfeiffer, R. D. Smith, Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests, Preventive Veterinary Medicine 45 (2000) 23. https://doi.org/10.1016/S0167-5877(00)00115-X [CrossRef] [PubMed] [Google Scholar]
  78. X. H. Zhou, D. K. McClish, N. A. Obuchowski, Statistical Methods in Diagnostic Medicine Wiley (2002). https://doi.org/10.1002/9780470317082 [CrossRef] [Google Scholar]
  79. P. Ray, Y. Le Manach, B. Riou, T. T. Houle, Statistical Evaluation of a Biomarker, Anesthesiology 112 (2010) 1023. https://doi.org/10.1097/ALN.0b013e3181d47604 [Google Scholar]
  80. K. Hajian-Tilaki, Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation, Caspian Journal of Internal Medicine 4 (2013) 627. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3755824 [PubMed] [Google Scholar]
  81. N. M. Adams, D. J. Hand, Comparing classifiers when the misallocation costs are uncertain, Pattern Recognition 32 (1999) 1139. https://doi.org/10.1016/S0031-3203(98)00154-X [Google Scholar]
  82. C. Drummond, R. C. Holte, Explicitly representing expected cost: an alternative to ROC representation, Proc. 6th Int. Conf. on Knowledge Discovery and Data Mining (KDD-00), Boston (2000). https://doi.org/10.1145/347090.347126 [Google Scholar]
  83. C. Drummond, R. C. Holte, Cost curves: An improved method for visualizing classifier performance, Mach. Learn. 65 (2006) 95. https://doi.org/10.1007/s10994-006-8199-5 [Google Scholar]
  84. J. Davis, M. Goadrich, The relationship between Precision-Recall and ROC curves, Proc. 23rd Int. Conf. on Machine Learning (ICML ’06), Pittsburgh (2006). https://doi.org/10.1145/1143844.1143874 [Google Scholar]
  85. T. Saito, M. Rehmsmeier, The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets, PLoS One 10 (2015) e0118432. https://doi.org/10.1371/journal.pone.0118432 [CrossRef] [PubMed] [Google Scholar]
  86. H. He, E. A. Garcia, Learning from Imbalanced Data, IEEE Trans. Knowl. Data Eng. 21 (2009) 1263. https://doi.org/10.1109/TKDE.2008.239 [Google Scholar]
  87. S. Clémençon, N. Vayatis, Ranking the Best Instances, J. Mach. Learn. Res. 8 (2007) 2671. http://www.jmlr.org/papers/v8/clemencon07a.html [Google Scholar]
  88. S. Clémençon, G. Lugosi, N. Vayatis, Ranking and Empirical Minimization of Ustatistics, Ann. Statist. 36 (2008) 844. https://doi.org/10.1214/009052607000000910 [CrossRef] [Google Scholar]
  89. C. Rudin, Y. Wang, Direct Learning to Rank And Rerank, Proc. 21st Int. Conf. on Artificial Intelligence and Statistics (AISTATS2018), PMLR 84 (2018) 775. http://proceedings.mlr.press/v84/rudin18a.html [Google Scholar]
  90. M. J. Pencina, R. B. D’Agostino, Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation, Statistics in Medicine 23 (2004) 2109. https://doi.org/10.1002/sim.1802 [CrossRef] [PubMed] [Google Scholar]
  91. N. A. Obuchowski, An ROC-Type Measure of Diagnostic Accuracy When the Gold Standard is Continuous-Scale, Statistics in Medicine 25 (2006) 481. https://doi.org/10.1002/sim.2228 [CrossRef] [PubMed] [Google Scholar]
  92. J. Lambert et al., How to Measure the Diagnostic Accuracy of Noninvasive Liver Fibrosis Indices: The Area Under the ROC Curve Revisited, Clinical Chemistry 54 (2008) 1372. https://doi.org/10.1373/clinchem.2007.097923 [CrossRef] [PubMed] [Google Scholar]
  93. K. Järvelin, J. Kekäläinen, IR evaluation methods for retrieving highly relevant documents, Proc. 23rd ACM SIGIR Conf. (SIGIR 2000), Athens (2000). https://doi.org/10.1145/345508.345545 [Google Scholar]
  94. J. Kekäläinen, K. Järvelin, Using graded relevance assessments in IR evaluation, J. Am. Soc. Inf. Sci. Tech. 53 (2002) 1120. https://doi.org/10.1002/asi.10137 [CrossRef] [Google Scholar]
  95. K. Järvelin, J. Kekäläinen, Cumulated gain-based evaluation of IR techniques, J. ACM Trans. on Inf. Sys. (TOIS) 20 (2002) 422. https://doi.org/10.1145/582415.582418 [CrossRef] [Google Scholar]
  96. P. D. Turney, Cost-sensitive classification: empirical evaluation of a hybrid genetic decision tree induction algorithm, J. Art. Intell. Res. 2 (1994) 369. https://doi.org/10.1613/jair.120 [Google Scholar]
  97. C. Drummond, R. C. Holte, Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria, Proc. 17th Int. Conf. on Machine Learning (ICML ’00), Stanford (2000). https://www.aaai.org/Library/Workshops/2000/ws00-05-009.php [Google Scholar]
  98. B. Zadrozny, C. Elkan, Learning and making decisions when costs and probabilities are both unknown, Proc. 7th Int. Conf. on Knowledge Discovery and Data Mining (KDD-01), San Francisco (2001). https://doi.org/10.1145/502512.502540 [Google Scholar]
  99. C. Elkan, The Foundations of Cost-Sensitive Learning, Proc. 17th Int. Joint Conf. on Artificial Intelligence (IJCAI-01), Seattle (2001). https://dl.acm.org/doi/abs/10.5555/1642194.1642224 [Google Scholar]
  100. B. Zadrozny, J. Langford, N. Abe, Cost-sensitive learning by cost-proportionate example weighting, Proc. 3rd IEEE Int. Conf. on Data Mining (ICDM-2003), Melbourne (2003). https://doi.org/10.1109/ICDM.2003.1250950 [Google Scholar]
  101. T. Fawcett, ROC graphs with instance-varying costs, Pattern Recognition Letters 27 (2006) 882. https://doi.org/10.1016/j.patrec.2005.10.012 [Google Scholar]
  102. B. Zadrozny, C. Elkan, Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers, Proc. 18th Int. Conf. on Machine Learning (ICML ’01), Williamstown (2001). http://cseweb.ucsd.edu/elkan/calibrated.pdf [Google Scholar]
  103. C. Guo, G. Pleiss, Y. Sun, K. Q. Weinberger, On calibration of modern neural networks, Proc. 34th Int. Conf. on Machine Learning (ICML ’17), Sydney (2017). https://arxiv.org/abs/1706.04599 [Google Scholar]
  104. F. Sanders, On Subjective Probability Forecasting, J. Applied Meteorology 2 (1963) 191. https://doi.org/10.1175/1520-0450(1963)002%3C0191:OSPF%3E2.0.CO;2 [CrossRef] [Google Scholar]
  105. A. H. Murphy, A New Vector Partition of the Probability Score, J. Applied Meteorology 12 (1973) 595. https://doi.org/10.1175/15200450(1973)012<0595:ANVPOT>2.0.CO;2 [CrossRef] [Google Scholar]
  106. S. Lichtenstein, B. Fischhoff, L. Phillips, Calibration of Probabilities: the State of the Art, DARPA Tech. Rep. DDI-3 (1976). https://apps.dtic.mil/dtic/tr/fulltext/u2/a033248.pdf [Google Scholar]
  107. I. Mason, A model for assessment of weather forecasts, Australian Meteorological Magazine 30 (1982) 291. http://www.bom.gov.au/jshess/docs/1982/mason.pdf [Google Scholar]
  108. A. H. Murphy, R. L. Winkler, A General Framework for Forecast Verification, Monthly Weather Review 115 (1987) 1330. https://doi.org/10.1175/15200493(1987)115%3C1330:AGFFFV%3E2.0.CO;2 [Google Scholar]
  109. World Meteorological Organization, Standardized Verification System (SVS) for Long-Range Forecasts (LRF), Attachment II.8 to WMO Manual N. 485 (2010). https://www.wmo.int/pages/prog/www/DPFS/documents/485_Vol_I_en_colour.pdf [Google Scholar]
  110. D. J. Spiegelhalter, Probabilistic prediction in patient management and clinical trials, Statist. Med. 5 (1986) 421. https://doi.org/10.1002/sim.4780050506 [CrossRef] [Google Scholar]
  111. F. E. Harrell, K. L. Lee, D. B. Mark, Tutorial in Biostatistics – Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors, Statist. Med. 15 (1996) 361. https://doi.org/10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4 [CrossRef] [PubMed] [Google Scholar]
  112. A. Valassi, Optimising HEP parameter fits through MC weight derivative regression, CHEP2019 presentation slides, Adelaide. https://doi.org/10.5281/zenodo.3523164 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.