{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:01:28Z","timestamp":1760709688821,"version":"3.41.0"},"publisher-location":"Cham","reference-count":58,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319997391"},{"type":"electronic","value":"9783319997407"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"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":[[2018]]},"DOI":"10.1007\/978-3-319-99740-7_24","type":"book-chapter","created":{"date-parts":[[2018,8,23]],"date-time":"2018-08-23T09:44:08Z","timestamp":1535017448000},"page":"329-349","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Measures of Model Interpretability for Model Selection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0062-5567","authenticated-orcid":false,"given":"Andr\u00e9","family":"Carrington","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul","family":"Fieguth","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Helen","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,8,24]]},"reference":[{"issue":"2","key":"24_CR1","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.asoc.2004.12.002","volume":"6","author":"S Ali","year":"2006","unstructured":"Ali, S., Smith, K.A.: On learning algorithm selection for classification. Appl. Soft Comput. 6(2), 119\u2013138 (2006)","journal-title":"Appl. Soft Comput."},{"key":"24_CR2","unstructured":"Auder, B., Iooss, B.: Global sensitivity analysis based on entropy. In: Proceedings of the ESREL 2008 Safety, reliability and risk analysis Conference, pp. 2107\u20132115 (2008)"},{"key":"24_CR3","doi-asserted-by":"crossref","unstructured":"Backhaus, A., Seiffert, U.: Quantitative measurements of model interpretability for the analysis of spectral data. In: IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 18\u201325. IEEE (2013)","DOI":"10.1109\/CIDM.2013.6597212"},{"key":"24_CR4","volume-title":"Complexity: Hierarchical Structures and Scaling in Physics","author":"R Badii","year":"1999","unstructured":"Badii, R., Politi, A.: Complexity: Hierarchical Structures and Scaling in Physics, vol. 6. Cambridge University Press, Cambridge (1999)"},{"key":"24_CR5","first-page":"1803","volume":"11","author":"D Baehrens","year":"2010","unstructured":"Baehrens, D., Schroeter, T., Harmeling, S., Kawanabe, M., Hansen, K., M\u00c3\u017eller, K.-R.: How to explain individual classification decisions. J. Mach. Learn. Res. 11, 1803\u20131831 (2010)","journal-title":"J. Mach. Learn. Res."},{"key":"24_CR6","doi-asserted-by":"crossref","unstructured":"Ben-Hur, A., Weston, J.: A user\u2019s guide to support vector machines. In: Data Mining Techniques for the Life Sciences, pp. 223\u2013239. Springer (2010)","DOI":"10.1007\/978-1-60327-241-4_13"},{"key":"24_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-38319-4","volume-title":"Clinical Decision Support Systems","author":"ES Berner","year":"2007","unstructured":"Berner, E.S.: Clinical Decision Support Systems. Springer, New York (2007). https:\/\/doi.org\/10.1007\/978-0-387-38319-4"},{"key":"24_CR8","doi-asserted-by":"crossref","unstructured":"Boughorbel, S., Tarel, J.-P., Boujemaa, N.: Conditionally positive definite kernels for SVM based image recognition. In: IEEE International Conference on Multimedia and Expo, ICME 2005, pp. 113\u2013116. IEEE (2005)","DOI":"10.1109\/ICME.2005.1521373"},{"key":"24_CR9","first-page":"1875","volume":"9","author":"ML Braun","year":"2008","unstructured":"Braun, M.L., Buhmann, J.M., M\u00c3\u017eller, K.-R.: On relevant dimensions in kernel feature spaces. J. Mach. Learn. Res. 9, 1875\u20131908 (2008)","journal-title":"J. Mach. Learn. Res."},{"issue":"3","key":"24_CR10","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1214\/ss\/1009213726","volume":"16","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16(3), 199\u2013231 (2001)","journal-title":"Stat. Sci."},{"key":"24_CR11","doi-asserted-by":"crossref","unstructured":"Carrington, A.M., Fieguth, P.W., Chen, H.H.: A new mercer sigmoid kernel for clinical data classification. In: 36th Annual International Conference on Engineering in Medicine and Biology Society (EMBC), pp. 6397\u20136401. IEEE (2014)","DOI":"10.1109\/EMBC.2014.6945092"},{"key":"24_CR12","doi-asserted-by":"crossref","unstructured":"Caruana, R., Niculescu-Mizil, A.: An empirical comparison of supervised learning algorithms. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 161\u2013168. ACM (2006)","DOI":"10.1145\/1143844.1143865"},{"key":"24_CR13","unstructured":"Cotter, A., Keshet, J., Srebro, N.: Explicit approximations of the Gaussian kernel. arXiv preprint arXiv:1109.4603 (2011)"},{"key":"24_CR14","volume-title":"Elements of Information Theory","author":"TM Cover","year":"2012","unstructured":"Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley, Hoboken (2012)"},{"key":"24_CR15","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1177\/117693510600200030","volume":"2","author":"JA Cruz","year":"2006","unstructured":"Cruz, J.A., Wishart, D.S.: Applications of machine learning in cancer prediction and prognosis. Cancer Inform. 2, 59\u201378 (2006)","journal-title":"Cancer Inform."},{"issue":"1","key":"24_CR16","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.chemolab.2008.11.005","volume":"96","author":"O Devos","year":"2009","unstructured":"Devos, O., Ruckebusch, C., Durand, A., Duponchel, L., Huvenne, J.-P.: Support vector machines (SVM) in near infrared (NIR) spectroscopy: focus on parameters optimization and model interpretation. Chemom. Intell. Lab. Syst. 96(1), 27\u201333 (2009)","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"24_CR17","unstructured":"Doshi-Velez, F., Kim, B.: Towards a rigorous science of interpretable machine learning (2017)"},{"issue":"4","key":"24_CR18","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1007\/BF01025868","volume":"57","author":"D Freedman","year":"1981","unstructured":"Freedman, D., Diaconis, P.: On the histogram as a density estimator: L$$_2$$ theory. Zeitschrift f\u00fcr Wahrscheinlichkeitstheorie und verwandte Gebiete 57(4), 453\u2013476 (1981)","journal-title":"Zeitschrift f\u00fcr Wahrscheinlichkeitstheorie und verwandte Gebiete"},{"issue":"1","key":"24_CR19","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1002\/(SICI)1099-0526(199609\/10)2:1<44::AID-CPLX10>3.0.CO;2-X","volume":"2","author":"M Gell-Mann","year":"1996","unstructured":"Gell-Mann, M., Lloyd, S.: Information measures, effective complexity, and total information. Complexity 2(1), 44\u201352 (1996)","journal-title":"Complexity"},{"key":"24_CR20","unstructured":"Goodman, B., Flaxman, S.: European union regulations on algorithmic decision-making and a \u201cright to explanation\u201d. In: 1st Workshop on Human Interpretability in Machine Learning, International Conference of Machine Learning (2016)"},{"key":"24_CR21","volume-title":"Feynman\u2019s Lost Lecture: The Motion of Planets Around the Sun","author":"DL Goodstein","year":"1996","unstructured":"Goodstein, D.L., Goodstein, J.R.: Feynman\u2019s Lost Lecture: The Motion of Planets Around the Sun, vol. 1. W. W. Norton & Company, New York (1996)"},{"key":"24_CR22","volume-title":"Clinical Decision Support: The Road Ahead","author":"RA Greenes","year":"2011","unstructured":"Greenes, R.A.: Clinical Decision Support: The Road Ahead. Academic Press, SanDiego (2011)"},{"key":"24_CR23","unstructured":"Hanson, K.M., Hemez, F.M.: Sensitivity analysis of model output. In: Proceedings of the 4th International Conference on Sensitivity Analysis of Model Output (SAMO 2004), Santa Fe, 8\u201311 March 2004. Los Alamos National Laboratory (2005)"},{"key":"24_CR24","unstructured":"Holzinger, A., Biemann, C., Pattichis, C.S., Kell, D.B.: What do we need to build explainable AI systems for the medical domain? arXiv preprint arXiv:1712.09923 (2017)"},{"key":"24_CR25","unstructured":"Jernigan, M.E., Fieguth, P.: Introduction to Pattern Recognition. University of Waterloo (2004)"},{"issue":"3","key":"24_CR26","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1093\/biomet\/33.3.239","volume":"33","author":"MG Kendall","year":"1945","unstructured":"Kendall, M.G.: The treatment of ties in ranking problems. Biometrika 33(3), 239\u2013251 (1945)","journal-title":"Biometrika"},{"key":"24_CR27","doi-asserted-by":"crossref","unstructured":"Lemaire, V., F\u00e9raud, R., Voisine, N.: Contact personalization using a score understanding method. In: IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), IJCNN 2008, pp. 649\u2013654. IEEE (2008)","DOI":"10.1109\/IJCNN.2008.4633863"},{"key":"24_CR28","unstructured":"Liang, P.: Provenance and contracts in machine learning. In: Proceedings of the 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016) (2016)"},{"key":"24_CR29","first-page":"296","volume":"98","author":"D Lin","year":"1998","unstructured":"Lin, D.: An information-theoretic definition of similarity. ICML 98, 296\u2013304 (1998)","journal-title":"ICML"},{"key":"24_CR30","unstructured":"Lipton, Z.C., et al.: The mythos of model interpretability. In: IEEE Spectrum (2016)"},{"key":"24_CR31","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/978-3-319-03200-9_2","volume-title":"Fuzzy Logic and Applications","author":"PJG Lisboa","year":"2013","unstructured":"Lisboa, P.J.G.: Interpretability in machine learning \u2013 principles and practice. In: Masulli, F., Pasi, G., Yager, R. (eds.) WILF 2013. LNCS (LNAI), vol. 8256, pp. 15\u201321. Springer, Cham (2013). https:\/\/doi.org\/10.1007\/978-3-319-03200-9_2"},{"issue":"2","key":"24_CR32","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1115\/1.2159025","volume":"128","author":"H Liu","year":"2006","unstructured":"Liu, H., Chen, W., Sudjianto, A.: Relative entropy based method for probabilistic sensitivity analysis in engineering design. J. Mech. Des. 128(2), 326\u2013336 (2006)","journal-title":"J. Mech. Des."},{"issue":"4","key":"24_CR33","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1109\/MCS.2001.939938","volume":"21","author":"S Lloyd","year":"2001","unstructured":"Lloyd, S.: Measures of complexity: a nonexhaustive list. IEEE Control Syst. Mag. 21(4), 7\u20138 (2001)","journal-title":"IEEE Control Syst. Mag."},{"key":"24_CR34","doi-asserted-by":"crossref","unstructured":"Lou, Y., Caruana, R., Gehrke, J.: Intelligible models for classification and regression. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 150\u2013158. ACM (2012)","DOI":"10.1145\/2339530.2339556"},{"key":"24_CR35","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/978-1-4419-1280-0_3","volume-title":"Data Mining. Annals of Information Systems","author":"D Martens","year":"2010","unstructured":"Martens, D., Baesens, B.: Building acceptable classification models. In: Stahlbock, R., Crone, S., Lessmann, S. (eds.) Data Mining. Annals of Information Systems, pp. 53\u201374. Springer, Boston (2010). https:\/\/doi.org\/10.1007\/978-1-4419-1280-0_3"},{"key":"24_CR36","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.patrec.2016.01.004","volume":"73","author":"J McDermott","year":"2016","unstructured":"McDermott, J., Forsyth, R.S.: Diagnosing a disorder in a classification benchmark. Pattern Recognit. Lett. 73, 41\u201343 (2016)","journal-title":"Pattern Recognit. Lett."},{"key":"24_CR37","doi-asserted-by":"crossref","unstructured":"Mercer, J.: Functions of positive and negative type, and their connection with the theory of integral equations. Philos. Trans. R. Soc. Lond. Ser. A 209, 415\u2013446 (1909). Containing papers of a mathematical or physical character","DOI":"10.1098\/rsta.1909.0016"},{"key":"24_CR38","unstructured":"Miller, T., Howe, P., Sonenberg, L.: Explainable AI: beware of inmates running the asylum. In: IJCAI-17 Workshop on Explainable AI (XAI), p. 36 (2017)"},{"key":"24_CR39","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.patcog.2016.11.008","volume":"65","author":"G Montavon","year":"2017","unstructured":"Montavon, G., Lapuschkin, S., Binder, A., Samek, W., M\u00fcller, K.-R.: Explaining nonlinear classification decisions with deep Taylor decomposition. Pattern Recognit. 65, 211\u2013222 (2017)","journal-title":"Pattern Recognit."},{"issue":"10","key":"24_CR40","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1089\/dna.2007.0590","volume":"26","author":"J Nahar","year":"2007","unstructured":"Nahar, J., Ali, S., Chen, Y.-P.P.: Microarray data classification using automatic SVM kernel selection. DNA Cell Biol. 26(10), 707\u2013712 (2007)","journal-title":"DNA Cell Biol."},{"issue":"1","key":"24_CR41","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1186\/s13040-017-0154-4","volume":"10","author":"RS Olson","year":"2017","unstructured":"Olson, R.S., La Cava, W., Orzechowski, P., Urbanowicz, R.J., Moore, J.H.: PMLB: a large benchmark suite for machine learning evaluation and comparison. BioData Min. 10(1), 36 (2017)","journal-title":"BioData Min."},{"key":"24_CR42","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.knosys.2015.10.011","volume":"92","author":"PS Perez","year":"2016","unstructured":"Perez, P.S., Nozawa, S.R., Macedo, A.A., Baranauskas, J.A.: Windowing improvements towards more comprehensible models. Knowl. Based Syst. 92, 9\u201322 (2016)","journal-title":"Knowl. Based Syst."},{"key":"24_CR43","unstructured":"Poulin, B., et al.: Visual explanation of evidence with additive classifiers. In: Proceedings of the National Conference On Artificial Intelligence, vol. 21, p. 1822. AAAI Press, Menlo Park (1999). MIT Press, Cambridge (2006)"},{"issue":"8","key":"24_CR44","doi-asserted-by":"publisher","first-page":"1034","DOI":"10.1097\/ACM.0000000000000681","volume":"90","author":"MV Pusic","year":"2015","unstructured":"Pusic, M.V., Boutis, K., Hatala, R., Cook, D.A.: Learning curves in health professions education. Acad. Med. 90(8), 1034\u20131042 (2015)","journal-title":"Acad. Med."},{"key":"24_CR45","unstructured":"R\u00e9nyi, A., et al.: On measures of entropy and information. In: Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Contributions to the Theory of Statistics. The Regents of the University of California (1961)"},{"key":"24_CR46","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: Why should i trust you? Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144. ACM (2016)","DOI":"10.1145\/2939672.2939778"},{"issue":"3","key":"24_CR47","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1093\/biomet\/66.3.605","volume":"66","author":"DW Scott","year":"1979","unstructured":"Scott, D.W.: On optimal and data-based histograms. Biometrika 66(3), 605\u2013610 (1979)","journal-title":"Biometrika"},{"key":"24_CR48","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780195172805.001.0001","volume-title":"Statistical Analysis of Epidemiologic Data","author":"S Selvin","year":"2004","unstructured":"Selvin, S.: Statistical Analysis of Epidemiologic Data. Oxford University Press, New York (2004)"},{"key":"24_CR49","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511809682","volume-title":"Kernel Methods for Pattern Analysis","author":"J Shawe-Taylor","year":"2004","unstructured":"Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, New York (2004)"},{"issue":"2","key":"24_CR50","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1007\/BF00166500","volume":"44","author":"E Sober","year":"1996","unstructured":"Sober, E.: Parsimony and predictive equivalence. Erkenntnis 44(2), 167\u2013197 (1996)","journal-title":"Erkenntnis"},{"issue":"1","key":"24_CR51","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/S0378-4754(00)00270-6","volume":"55","author":"IM Sobol","year":"2001","unstructured":"Sobol, I.M.: Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math. Comput. Simul. 55(1), 271\u2013280 (2001)","journal-title":"Math. Comput. Simul."},{"key":"24_CR52","doi-asserted-by":"crossref","unstructured":"Stevens, S.S.: On the theory of scales of measurement (1946)","DOI":"10.1126\/science.103.2684.677"},{"issue":"153","key":"24_CR53","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1080\/01621459.1926.10502161","volume":"21","author":"HA Sturges","year":"1926","unstructured":"Sturges, H.A.: The choice of a class interval. J. Am. Stat. Assoc. 21(153), 65\u201366 (1926)","journal-title":"J. Am. Stat. Assoc."},{"key":"24_CR54","first-page":"1063","volume":"8","author":"Z Szab\u00f3","year":"2007","unstructured":"Szab\u00f3, Z., P\u00f3czos, B., L\u0151rincz, A.: Undercomplete blind subspace deconvolution. J. Mach. Learn. Res. 8, 1063\u20131095 (2007)","journal-title":"J. Mach. Learn. Res."},{"key":"24_CR55","doi-asserted-by":"publisher","first-page":"1782","DOI":"10.1016\/j.patcog.2011.09.007","volume":"45","author":"Z Szab\u00f3","year":"2012","unstructured":"Szab\u00f3, Z., P\u00f3czos, B., L\u0151rincz, A.: Separation theorem for independent subspace analysis and its consequences. Pattern Recognit. 45, 1782\u20131791 (2012)","journal-title":"Pattern Recognit."},{"issue":"1","key":"24_CR56","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1007\/BF01016429","volume":"52","author":"C Tsallis","year":"1988","unstructured":"Tsallis, C.: Possible generalization of Boltzmann-Gibbs statistics. J. Stat. Phys. 52(1), 479\u2013487 (1988)","journal-title":"J. Stat. Phys."},{"key":"24_CR57","unstructured":"Tussy, A., Gustafson, R.: Elementary Algebra. Nelson Education (2012)"},{"issue":"10","key":"24_CR58","doi-asserted-by":"publisher","first-page":"1087","DOI":"10.1016\/j.jclinepi.2006.01.014","volume":"59","author":"ART Donders","year":"2006","unstructured":"Donders, A.R.T., Van Der Heijden, G.J.M.G., Stijnen, T., Moons, K.G.M.: A gentle introduction to imputation of missing values. J. clin. epidemiol. 59(10), 1087\u20131091 (2006). Elsevier","journal-title":"J. clin. epidemiol."}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-99740-7_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,6]],"date-time":"2025-07-06T13:59:01Z","timestamp":1751810341000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-99740-7_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783319997391","9783319997407"],"references-count":58,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-99740-7_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"24 August 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CD-MAKE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Cross-Domain Conference for Machine Learning and Knowledge Extraction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hamburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 August 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cd-make2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cd-make.net\/about\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"45","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"25","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"56% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}