{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T18:09:21Z","timestamp":1755799761096,"version":"3.44.0"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030003739"},{"type":"electronic","value":"9783030003746"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","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\/978-3-030-00374-6_6","type":"book-chapter","created":{"date-parts":[[2018,9,25]],"date-time":"2018-09-25T23:43:45Z","timestamp":1537919025000},"page":"55-65","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Identifying the Machine Learning Family from Black-Box Models"],"prefix":"10.1007","author":[{"given":"Ra\u00fcl","family":"Fabra-Boluda","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C\u00e8sar","family":"Ferri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9","family":"Hern\u00e1ndez-Orallo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fernando","family":"Mart\u00ednez-Plumed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mar\u00eda Jos\u00e9","family":"Ram\u00edrez-Quintana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,9,27]]},"reference":[{"issue":"4","key":"6_CR1","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1023\/A:1022821128753","volume":"2","author":"D Angluin","year":"1988","unstructured":"Angluin, D.: Queries and concept learning. Mach. Learn. 2(4), 319\u2013342 (1988)","journal-title":"Mach. Learn."},{"issue":"2","key":"6_CR2","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1016\/0304-3975(91)90026-X","volume":"86","author":"GM Benedek","year":"1991","unstructured":"Benedek, G.M., Itai, A.: Learnability with respect to fixed distributions. Theor. Comput. Sci. 86(2), 377\u2013389 (1991)","journal-title":"Theor. Comput. Sci."},{"key":"6_CR3","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/978-3-319-02300-7_4","volume-title":"Support Vector Machines Applications","author":"B Biggio","year":"2014","unstructured":"Biggio, B., et al.: Security Evaluation of support vector machines in adversarial environments. In: Ma, Y., Guo, G. (eds.) Support Vector Machines Applications, pp. 105\u2013153. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-02300-7_4"},{"key":"6_CR4","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1007\/978-3-540-30214-8_29","volume-title":"Discovery Science","author":"R Blanco-Vega","year":"2004","unstructured":"Blanco-Vega, R., Hern\u00e1ndez-Orallo, J., Ram\u00edrez-Quintana, M.J.: Analysing the trade-off between comprehensibility and accuracy in mimetic models. In: Suzuki, E., Arikawa, S. (eds.) DS 2004. LNCS (LNAI), vol. 3245, pp. 338\u2013346. Springer, Heidelberg (2004). https:\/\/doi.org\/10.1007\/978-3-540-30214-8_29"},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"Dalvi, N., Domingos, P., Sanghai, S., Verma, D., et al.: Adversarial classification. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 99\u2013108. ACM (2004)","DOI":"10.1145\/1014052.1014066"},{"key":"6_CR6","unstructured":"Dheeru, D., Karra Taniskidou, E.: UCI machine learning repository (2017). http:\/\/archive.ics.uci.edu\/ml"},{"issue":"3","key":"6_CR7","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(3), 187\u2013202 (1998)","journal-title":"Intell. Data Anal."},{"key":"6_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1007\/978-3-642-17711-8_5","volume-title":"Recognizing Patterns in Signals, Speech, Images and Videos","author":"RPW Duin","year":"2010","unstructured":"Duin, R.P.W., Loog, M., P\u0229kalska, E., Tax, D.M.J.: Feature-based dissimilarity space classification. In: \u00dcnay, D., \u00c7ataltepe, Z., Aksoy, S. (eds.) ICPR 2010. LNCS, vol. 6388, pp. 46\u201355. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-17711-8_5"},{"issue":"1","key":"6_CR9","first-page":"3133","volume":"15","author":"M Fern\u00e1ndez-Delgado","year":"2014","unstructured":"Fern\u00e1ndez-Delgado, M., Cernadas, E., Barro, S., Amorim, D.: Do we need hundreds of classifiers to solve real world classification problems. J. Mach. Learn. Res. 15(1), 3133\u20133181 (2014)","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"6_CR10","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 Recognit. Lett. 30(1), 27\u201338 (2009)","journal-title":"Pattern Recognit. Lett."},{"issue":"1","key":"6_CR11","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.inffus.2006.10.002","volume":"9","author":"G Giacinto","year":"2008","unstructured":"Giacinto, G., Perdisci, R., Del Rio, M., Roli, F.: Intrusion detection in computer networks by a modular ensemble of one-class classifiers. Inf. Fusion 9(1), 69\u201382 (2008)","journal-title":"Inf. Fusion"},{"key":"6_CR12","doi-asserted-by":"crossref","unstructured":"Huang, L., Joseph, A.D., Nelson, B., Rubinstein, B.I., Tygar, J.: Adversarial machine learning. In: Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence, pp. 43\u201358 (2011)","DOI":"10.1145\/2046684.2046692"},{"issue":"2","key":"6_CR13","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1023\/A:1022859003006","volume":"51","author":"LI Kuncheva","year":"2003","unstructured":"Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach. Learn. 51(2), 181\u2013207 (2003)","journal-title":"Mach. Learn."},{"key":"6_CR14","doi-asserted-by":"publisher","first-page":"363","DOI":"10.2307\/2529786","volume":"33","author":"JR Landis","year":"1977","unstructured":"Landis, J.R., Koch, G.G.: An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics 33, 363\u2013374 (1977)","journal-title":"Biometrics"},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Lowd, D., Meek, C.: Adversarial learning. In: Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data mining, pp. 641\u2013647. ACM (2005)","DOI":"10.1145\/1081870.1081950"},{"key":"6_CR16","unstructured":"Mart\u0131nez-Plumed, F., Prud\u00eancio, R.B., Mart\u0131nez-Us\u00f3, A., Hern\u00e1ndez-Orallo, J.: Making sense of item response theory in machine learning. In: Proceedings of 22nd European Conference on Artificial Intelligence (ECAI). Frontiers in Artificial Intelligence and Applications, vol. 285, pp. 1140\u20131148 (2016)"},{"key":"6_CR17","unstructured":"Papernot, N., McDaniel, P., Goodfellow, I.: Transferability in machine learning: from phenomena to black-box attacks using adversarial samples. arXiv preprint arXiv:1605.07277 (2016)"},{"key":"6_CR18","doi-asserted-by":"crossref","unstructured":"Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z.B., Swami, A.: The limitations of deep learning in adversarial settings. In: 2016 IEEE European Symposium on Security and Privacy (EuroS&P), pp. 372\u2013387. IEEE (2016)","DOI":"10.1109\/EuroSP.2016.36"},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Papernot, N., McDaniel, P., Wu, X., Jha, S., Swami, A.: Distillation as a defense to adversarial perturbations against deep neural networks. In: 2016 IEEE Symposium on Security and Privacy (SP), pp. 582\u2013597. IEEE (2016)","DOI":"10.1109\/SP.2016.41"},{"issue":"1","key":"6_CR20","first-page":"21","volume":"5","author":"MP Sesmero","year":"2015","unstructured":"Sesmero, M.P., Ledezma, A.I., Sanchis, A.: Generating ensembles of heterogeneous classifiers using stacked generalization. Wiley Interdiscip. Rev.: Data Min. Knowl. Discov. 5(1), 21\u201334 (2015)","journal-title":"Wiley Interdiscip. Rev.: Data Min. Knowl. Discov."},{"issue":"2","key":"6_CR21","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1007\/s10994-013-5422-z","volume":"95","author":"MR Smith","year":"2014","unstructured":"Smith, M.R., Martinez, T., Giraud-Carrier, C.: An instance level analysis of data complexity. Mach. Learn. 95(2), 225\u2013256 (2014)","journal-title":"Mach. Learn."},{"key":"6_CR22","unstructured":"Tram\u00e8r, F., Zhang, F., Juels, A., Reiter, M.K., Ristenpart, T.: Stealing machine learning models via prediction APIs. In: USENIX Security Symposium, pp. 601\u2013618 (2016)"},{"issue":"11","key":"6_CR23","doi-asserted-by":"publisher","first-page":"1134","DOI":"10.1145\/1968.1972","volume":"27","author":"LG Valiant","year":"1984","unstructured":"Valiant, L.G.: A theory of the learnable. Commun. ACM 27(11), 1134\u20131142 (1984)","journal-title":"Commun. ACM"},{"issue":"2","key":"6_CR24","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1093\/comjnl\/11.2.185","volume":"11","author":"CS Wallace","year":"1968","unstructured":"Wallace, C.S., Boulton, D.M.: An information measure for classification. Comput. J. 11(2), 185\u2013194 (1968)","journal-title":"Comput. J."},{"issue":"2","key":"6_CR25","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","volume":"5","author":"DH Wolpert","year":"1992","unstructured":"Wolpert, D.H.: Stacked generalization. Neural Netw. 5(2), 241\u2013259 (1992)","journal-title":"Neural Netw."}],"container-title":["Lecture Notes in Computer Science","Advances in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-00374-6_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T04:50:26Z","timestamp":1755665426000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-00374-6_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030003739","9783030003746"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-00374-6_6","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":"27 September 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CAEPIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Conference of the Spanish Association for Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Granada","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","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":"23 October 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 October 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"caepia2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/sci2s.ugr.es\/caepia18\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"240","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":"36","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":"15% - 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":"3,1","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":"2,1","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)"}}]}}