{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T08:06:23Z","timestamp":1745568383694},"publisher-location":"Cham","reference-count":34,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319964478"},{"type":"electronic","value":"9783319964485"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-96448-5_16","type":"book-chapter","created":{"date-parts":[[2018,8,28]],"date-time":"2018-08-28T12:53:27Z","timestamp":1535460807000},"page":"175-186","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Modelling Machine Learning Models"],"prefix":"10.1007","author":[{"given":"Ra\u00fcl","family":"Fabra-Boluda","sequence":"first","affiliation":[]},{"given":"C\u00e8sar","family":"Ferri","sequence":"additional","affiliation":[]},{"given":"Jos\u00e9","family":"Hern\u00e1ndez-Orallo","sequence":"additional","affiliation":[]},{"given":"Fernando","family":"Mart\u00ednez-Plumed","sequence":"additional","affiliation":[]},{"given":"M. Jos\u00e9","family":"Ram\u00edrez-Quintana","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,8,29]]},"reference":[{"key":"16_CR1","volume-title":"Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications","author":"V Balasubramanian","year":"2014","unstructured":"Balasubramanian, V., Ho, S.-S., Vovk, V.: Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications. Newnes, Oxford (2014)"},{"issue":"2","key":"16_CR2","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s00607-010-0129-5","volume":"91","author":"A Bella","year":"2011","unstructured":"Bella, A., Ferri, C., Hern\u00e1ndez-Orallo, J., Ram\u00edrez-Quintana, M.J.: Using negotiable features for prescription problems. Computing 91(2), 135\u2013168 (2011)","journal-title":"Computing"},{"issue":"4","key":"16_CR3","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1007\/s10489-012-0388-2","volume":"38","author":"A Bella","year":"2013","unstructured":"Bella, A., Ferri, C., Hern\u00e1ndez-Orallo, J., Ram\u00edrez-Quintana, M.J.: On the effect of calibration in classifier combination. Appl. Intell. 38(4), 566\u2013585 (2013)","journal-title":"Appl. Intell."},{"key":"16_CR4","unstructured":"Blanco-Vega, R., Ferri-Ram\u00edrez, C., Hern\u00e1ndez-Orallo, J., Ram\u00edrez-Quintana, M.: Estimating the class probability threshold without training data. In: Workshop on ROC Analysis in Machine Learning, p. 9 (2006)"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Blanco-Vega, R., Hern\u00e1ndez-Orallo, J., Ram\u00edrez-Quintana, M.: Analysing the trade-off between comprehensibility and accuracy in mimetic models. In: Discovery Science, pp. 35\u201339 (2004)","DOI":"10.1007\/978-3-540-30214-8_29"},{"key":"16_CR6","volume-title":"Computer Models of Mind: Computational Approaches in Theoretical Psychology","author":"MA Boden","year":"1988","unstructured":"Boden, M.A.: Computer Models of Mind: Computational Approaches in Theoretical Psychology. Cambridge University Press, New York (1988)"},{"key":"16_CR7","volume-title":"A Theory of Understanding: Philosophical and Psychological Perspectives","author":"D Chart","year":"2000","unstructured":"Chart, D.: A Theory of Understanding: Philosophical and Psychological Perspectives. Routledge, New York (2000)"},{"key":"16_CR8","doi-asserted-by":"crossref","unstructured":"Core, M., Lane, H.C., van Lent, M., Gomboc, D., Solomon, S., Rosenberg, M.: Building explainable artificial intelligence systems. In: Proceedings of the 18th Innovative Applications of Artificial Intelligence Conference (2006)","DOI":"10.21236\/ADA459166"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Cousot, P., Cousot, R.: Abstract interpretation: a unified lattice model for static analysis of programs by construction or approximation of fixpoints. In: Proceedings of the 4th ACM SIGACT-SIGPLAN Symposium on Principles of Programming Languages, pp. 238\u2013252 (1977)","DOI":"10.1145\/512950.512973"},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Cui, Z., Chen, W., He, Y., Chen, Y.: Optimal action extraction for random forests and boosted trees. In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 179\u2013188 (2015)","DOI":"10.1145\/2783258.2783281"},{"issue":"1\u20134","key":"16_CR11","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/S1088-467X(98)00023-7","volume":"2","author":"P Domingos","year":"1998","unstructured":"Domingos, P.: Knowledge discovery via multiple models. Intell. Data Anal. 2(1\u20134), 187\u2013202 (1998)","journal-title":"Intell. Data Anal."},{"key":"16_CR12","unstructured":"Doshi-Velez, F., Kim, B.: Towards a rigorous science of interpretable machine learning. arXiv (2017)"},{"key":"16_CR13","unstructured":"Elkan, C.: The foundations of cost-sensitive learning. In: International Joint Conference on Artificial Intelligence, vol. 17, pp. 973\u2013978 (2001)"},{"issue":"2","key":"16_CR14","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/S0164-1212(99)00035-7","volume":"47","author":"NE Fenton","year":"1999","unstructured":"Fenton, N.E., Neil, M.: Software metrics: successes, failures and new directions. J. Syst. Softw. 47(2), 149\u2013157 (1999)","journal-title":"J. Syst. Softw."},{"key":"16_CR15","doi-asserted-by":"crossref","unstructured":"Ferri, C., Hern\u00e1ndez-Orallo, J., Ram\u00edrez-Quintana, M.J.: From ensemble methods to comprehensible models. In: 5th International Conference on Discovery Science, pp. 165\u2013177 (2002)","DOI":"10.1007\/3-540-36182-0_16"},{"key":"16_CR16","unstructured":"Ferri, C., Hern\u00e1ndez-Orallo, J., Mart\u00ednez-Us\u00f3, A., Ram\u00edrez-Quintana, M.: Identifying dominant models when the noise context is known. In: First Workshop on Generalization and Reuse of Machine Learning Models Over Multiple Contexts (2014)"},{"issue":"1","key":"16_CR17","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.patrec.2008.08.010","volume":"30","author":"C Ferri","year":"2009","unstructured":"Ferri, C., Hern\u00e1ndez-Orallo, J., Modroiu, R.: An experimental comparison of performance measures for classification. Pattern Recogn. Lett. 30(1), 27\u201338 (2009)","journal-title":"Pattern Recogn. Lett."},{"key":"16_CR18","first-page":"2813","volume":"13","author":"J Hern\u00e1ndez-Orallo","year":"2012","unstructured":"Hern\u00e1ndez-Orallo, J., Flach, P.A., Ferri, C.: A unified view of performance metrics: translating threshold choice into expected classification loss. J. Mach. Learn. Res. 13, 2813\u20132869 (2012)","journal-title":"J. Mach. Learn. Res."},{"key":"16_CR19","doi-asserted-by":"publisher","DOI":"10.1017\/9781316594179","volume-title":"The Measure of All Minds: Evaluating Natural and Artificial Intelligence","author":"J Hern\u00e1ndez-Orallo","year":"2017","unstructured":"Hern\u00e1ndez-Orallo, J.: The Measure of All Minds: Evaluating Natural and Artificial Intelligence. Cambridge University Press, New York (2017)"},{"issue":"5","key":"16_CR20","doi-asserted-by":"publisher","first-page":"551","DOI":"10.3233\/AIC-160705","volume":"29","author":"J Hern\u00e1ndez-Orallo","year":"2016","unstructured":"Hern\u00e1ndez-Orallo, J., Mart\u00ednez-Us\u00f3, A., Prud\u00eancio, R.B., Kull, M., Flach, P., Farhan Ahmed, C., Lachiche, N.: Reframing in context: a systematic approach for model reuse in machine learning. AI Commun. 29(5), 551\u2013566 (2016)","journal-title":"AI Commun."},{"key":"16_CR21","doi-asserted-by":"crossref","unstructured":"Huang, L., Joseph, A.D., Nelson, B., Rubinstein, B.I., Tygar, J.D.: Adversarial machine learning. In: Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence, pp. 43\u201358 (2011)","DOI":"10.1145\/2046684.2046692"},{"key":"16_CR22","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/6610.001.0001","volume-title":"Systems that Learn","author":"S Jain","year":"1999","unstructured":"Jain, S., Osherson, D., Royer, J.S., Sharma, A.: Systems that Learn, 2nd edn. MIT Press, Cambridge (1999)","edition":"2"},{"key":"16_CR23","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511921803","volume-title":"Evaluating Learning Algorithms: A Classification Perspective","author":"N Japkowicz","year":"2011","unstructured":"Japkowicz, N., Shah, M.: Evaluating Learning Algorithms: A Classification Perspective. Cambridge University Press, New York (2011)"},{"key":"16_CR24","doi-asserted-by":"crossref","unstructured":"Kamiran, F., Calders, T.: Classifying without discriminating. In: 2nd International Conference on Computer, Control and Communication, pp. 1\u20136 (2009)","DOI":"10.1109\/IC4.2009.4909197"},{"key":"16_CR25","doi-asserted-by":"crossref","unstructured":"Langley, P., et al.: Selection of relevant features in machine learning. In: Proceedings of the AAAI Fall Symposium on Relevance, vol. 184, pp. 245\u2013271 (1994)","DOI":"10.21236\/ADA292575"},{"key":"16_CR26","unstructured":"Lichman, M.: UCI machine learning repository (2013)"},{"key":"16_CR27","unstructured":"Lyu, Q., Chen, Y., Li, Z., Cui, Z., Chen, L., Zhang, X., Shen, H.: Extracting actionability from machine learning models by sub-optimal deterministic planning. arXiv preprint arXiv:1611.00873 (2016)"},{"key":"16_CR28","unstructured":"Mart\u00ednez-Plumed, F., Prud\u00eancio, R.B.C., Us\u00f3, A.M., Hern\u00e1ndez-Orallo, J.: Making sense of item response theory in machine learning. In: European Conference on Artificial Intelligence, pp. 1140\u20131148 (2016)"},{"key":"16_CR29","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 (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"16_CR30","unstructured":"Samek, W., Wiegand, T., M\u00fcller, K.-R.: Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models, ArXiv e-prints (2017)"},{"issue":"6","key":"16_CR31","doi-asserted-by":"publisher","first-page":"1057","DOI":"10.1109\/72.728352","volume":"9","author":"AB Tickle","year":"1998","unstructured":"Tickle, A.B., Andrews, R., Golea, M., Diederich, J.: The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks. Trans. Neur. Netw. 9(6), 1057\u20131068 (1998)","journal-title":"Trans. Neur. Netw."},{"key":"16_CR32","unstructured":"Turney, P.D.: The management of context-sensitive features: a review of strategies. arXiv preprint cs\/0212037 (2002)"},{"key":"16_CR33","unstructured":"Weller, A.: Challenges for transparency. arXiv preprint arXiv:1708.01870 (2017)"},{"issue":"1","key":"16_CR34","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/TKDE.2007.250584","volume":"19","author":"Q Yang","year":"2007","unstructured":"Yang, Q., Yin, J., Ling, C., Pan, R.: Extracting actionable knowledge from decision trees. IEEE Trans. Knowl. Data Eng. 19(1), 43\u201356 (2007)","journal-title":"IEEE Trans. Knowl. Data Eng."}],"container-title":["Studies in Applied Philosophy, Epistemology and Rational Ethics","Philosophy and Theory of Artificial Intelligence 2017"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-96448-5_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,8]],"date-time":"2020-11-08T10:57:46Z","timestamp":1604833066000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-96448-5_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783319964478","9783319964485"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-96448-5_16","relation":{},"ISSN":["2192-6255","2192-6263"],"issn-type":[{"type":"print","value":"2192-6255"},{"type":"electronic","value":"2192-6263"}],"subject":[],"published":{"date-parts":[[2018]]}}}