{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T02:34:08Z","timestamp":1778812448851,"version":"3.51.4"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030161477","type":"print"},{"value":"9783030161484","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-16148-4_6","type":"book-chapter","created":{"date-parts":[[2019,4,4]],"date-time":"2019-04-04T02:50:37Z","timestamp":1554346237000},"page":"69-80","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["On Calibration of Nested Dichotomies"],"prefix":"10.1007","author":[{"given":"Tim","family":"Leathart","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eibe","family":"Frank","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bernhard","family":"Pfahringer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Geoffrey","family":"Holmes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,3,22]]},"reference":[{"key":"6_CR1","doi-asserted-by":"crossref","unstructured":"Acharya, S., Pant, A.K., Gyawali, P.K.: Deep learning based large scale handwritten Devanagari character recognition. In: SKIMA, pp. 1\u20136. IEEE (2015)","DOI":"10.1109\/SKIMA.2015.7400041"},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"Agrawal, R., Gupta, A., Prabhu, Y., Varma, M.: Multi-label learning with millions of labels: recommending advertiser bid phrases for web pages. In: WWW, pp. 13\u201324 (2013)","DOI":"10.1145\/2488388.2488391"},{"key":"6_CR3","unstructured":"Bengio, S., Weston, J., Grangier, D.: Label embedding trees for large multi-class tasks. In: NIPS, pp. 163\u2013171 (2010)"},{"key":"6_CR4","doi-asserted-by":"crossref","unstructured":"Bennett, P.N., Nguyen, N.: Refined experts: improving classification in large taxonomies. In: SIGIR, pp. 11\u201318. ACM (2009)","DOI":"10.1145\/1571941.1571946"},{"key":"6_CR5","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1007\/978-3-642-04414-4_22","volume-title":"Algorithmic Learning Theory","author":"A Beygelzimer","year":"2009","unstructured":"Beygelzimer, A., Langford, J., Ravikumar, P.: Error-correcting tournaments. In: Gavald\u00e0, R., Lugosi, G., Zeugmann, T., Zilles, S. (eds.) ALT 2009. LNCS (LNAI), vol. 5809, pp. 247\u2013262. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-04414-4_22"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Bingham, E., Mannila, H.: Random projection in dimensionality reduction: applications to image and text data. In: KDD, pp. 245\u2013250. ACM (2001)","DOI":"10.1145\/502512.502546"},{"issue":"3","key":"6_CR7","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1145\/1961189.1961199","volume":"2","author":"CC Chang","year":"2011","unstructured":"Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27 (2011)","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"6_CR8","unstructured":"Choromanska, A.E., Langford, J.: Logarithmic time online multiclass prediction. In: NIPS, pp. 55\u201363 (2015)"},{"key":"6_CR9","unstructured":"Daum\u00e9, III, H., Karampatziakis, N., Langford, J., Mineiro, P.: Logarithmic time one-against-some. In: ICML, pp. 923\u2013932. PMLR (2017)"},{"key":"6_CR10","unstructured":"Dekel, O., Shamir, O.: Multiclass-multilabel classification with more classes than examples. In: AISTATS, pp. 137\u2013144. PMLR (2010)"},{"key":"6_CR11","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1007\/978-3-319-46227-1_32","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"K Dembczy\u0144ski","year":"2016","unstructured":"Dembczy\u0144ski, K., Kot\u0142owski, W., Waegeman, W., Busa-Fekete, R., H\u00fcllermeier, E.: Consistency of probabilistic classifier trees. In: Frasconi, P., Landwehr, N., Manco, G., Vreeken, J. (eds.) ECML PKDD 2016. LNCS (LNAI), vol. 9852, pp. 511\u2013526. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46227-1_32"},{"key":"6_CR12","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1613\/jair.105","volume":"2","author":"TG Dietterich","year":"1995","unstructured":"Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. JAIR 2, 263\u2013286 (1995)","journal-title":"JAIR"},{"key":"6_CR13","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1007\/11564126_13","volume-title":"Knowledge Discovery in Databases: PKDD 2005","author":"L Dong","year":"2005","unstructured":"Dong, L., Frank, E., Kramer, S.: Ensembles of balanced nested dichotomies for multi-class problems. In: Jorge, A.M., Torgo, L., Brazdil, P., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 84\u201395. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11564126_13"},{"key":"6_CR14","volume-title":"Applied Regression Analysis, Linear Models, and Related Methods","author":"J Fox","year":"1997","unstructured":"Fox, J.: Applied Regression Analysis, Linear Models, and Related Methods. Sage, Thousand Oaks (1997)"},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Frank, E., Kramer, S.: Ensembles of nested dichotomies for multi-class problems. In: ICML, pp. 39\u201346. ACM (2004)","DOI":"10.1145\/1015330.1015363"},{"key":"6_CR16","unstructured":"Friedman, J.H.: Another approach to polychotomous classification. Technical report, Statistics Department, Stanford University (1996)"},{"key":"6_CR17","unstructured":"Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: ICML, pp. 1321\u20131330. PMLR (2017)"},{"issue":"3","key":"6_CR18","doi-asserted-by":"publisher","first-page":"349","DOI":"10.4310\/SII.2009.v2.n3.a8","volume":"2","author":"T Hastie","year":"2009","unstructured":"Hastie, T., Rosset, S., Zhu, J., Zou, H.: Multi-class adaboost. Stat. Interface 2(3), 349\u2013360 (2009)","journal-title":"Stat. Interface"},{"key":"6_CR19","unstructured":"Jiang, X., Osl, M., Kim, J., Ohno-Machado, L.: Smooth isotonic regression: a new method to calibrate predictive models. In: AMIA Summits on Translational Science Proceedings, p. 16 (2011)"},{"issue":"1","key":"6_CR20","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/s10994-013-5371-6","volume":"92","author":"A Kumar","year":"2013","unstructured":"Kumar, A., Vembu, S., Menon, A.K., Elkan, C.: Beam search algorithms for multilabel learning. Mach. Learn. 92(1), 65\u201389 (2013)","journal-title":"Mach. Learn."},{"key":"6_CR21","unstructured":"Leathart, T., Frank, E., Pfahringer, B., Holmes, G.: Probability calibration trees. In: ACML, pp. 145\u2013160. PMLR (2017)"},{"key":"6_CR22","doi-asserted-by":"crossref","unstructured":"Leathart, T., Frank, E., Pfahringer, B., Holmes, G.: Ensembles of nested dichotomies with multiple subset evaluation. In: Yang, Q., et al. (eds.) PAKDD 2019. LNAI, vol. 11439, pp. xx-yy. Springer, Heidelberg (2019)","DOI":"10.1007\/978-3-030-16148-4_7"},{"key":"6_CR23","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/978-3-319-46227-1_12","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"T Leathart","year":"2016","unstructured":"Leathart, T., Pfahringer, B., Frank, E.: Building ensembles of adaptive nested dichotomies with random-pair selection. In: Frasconi, P., Landwehr, N., Manco, G., Vreeken, J. (eds.) ECML PKDD 2016. LNCS (LNAI), vol. 9852, pp. 179\u2013194. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46227-1_12"},{"key":"6_CR24","unstructured":"Lichman, M.: UCI machine learning repository (2013)"},{"issue":"9","key":"6_CR25","doi-asserted-by":"publisher","first-page":"1280","DOI":"10.1093\/bioinformatics\/btu022","volume":"30","author":"P Mah\u00e9","year":"2014","unstructured":"Mah\u00e9, P., et al.: Automatic identification of mixed bacterial species fingerprints in a MALDI-TOF mass-spectrum. Bioinformatics 30(9), 1280\u20131286 (2014)","journal-title":"Bioinformatics"},{"issue":"8\u201310","key":"6_CR26","first-page":"1","volume":"107","author":"V Melnikov","year":"2018","unstructured":"Melnikov, V., H\u00fcllermeier, E.: On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. Mach. Learn. 107(8\u201310), 1\u201324 (2018)","journal-title":"Mach. Learn."},{"key":"6_CR27","unstructured":"Mena, D., Monta\u00f1\u00e9s, E., Quevedo, J.R., Del Coz, J.J.: Using A* for inference in probabilistic classifier chains. In: IJCAI (2015)"},{"key":"6_CR28","doi-asserted-by":"publisher","first-page":"41","DOI":"10.2307\/2346866","volume":"26","author":"AH Murphy","year":"1977","unstructured":"Murphy, A.H., Winkler, R.L.: Reliability of subjective probability forecasts of precipitation and temperature. Appl. Stat. 26, 41\u201347 (1977)","journal-title":"Appl. Stat."},{"key":"6_CR29","unstructured":"Naeini, M., Cooper, G., Hauskrecht, M.: Obtaining well calibrated probabilities using Bayesian binning. In: AAAI, pp. 2901\u20132907 (2015)"},{"key":"6_CR30","doi-asserted-by":"crossref","unstructured":"Niculescu-Mizil, A., Caruana, R.: Predicting good probabilities with supervised learning. In: ICML, pp. 625\u2013632. ACM (2005)","DOI":"10.1145\/1102351.1102430"},{"issue":"Oct","key":"6_CR31","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in python. JMLR 12(Oct), 2825\u20132830 (2011)","journal-title":"JMLR"},{"issue":"3","key":"6_CR32","first-page":"61","volume":"10","author":"J Platt","year":"1999","unstructured":"Platt, J.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv. Large Margin Classif. 10(3), 61\u201374 (1999)","journal-title":"Adv. Large Margin Classif."},{"key":"6_CR33","first-page":"101","volume":"5","author":"R Rifkin","year":"2004","unstructured":"Rifkin, R., Klautau, A.: In defense of one-vs-all classification. JMLR 5, 101\u2013141 (2004)","journal-title":"JMLR"},{"issue":"3","key":"6_CR34","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. IJCV 115(3), 211\u2013252 (2015)","journal-title":"IJCV"},{"key":"6_CR35","doi-asserted-by":"crossref","unstructured":"Wever, M., Mohr, F., H\u00fcllermeier, E.: Ensembles of evolved nested dichotomies for classification. In: GECCO, pp. 561\u2013568. ACM (2018)","DOI":"10.1145\/3205455.3205562"},{"key":"6_CR36","unstructured":"Zadrozny, B., Elkan, C.: Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers. In: ICML, pp. 609\u2013616. ACM (2001)"},{"key":"6_CR37","doi-asserted-by":"crossref","unstructured":"Zadrozny, B., Elkan, C.: Transforming classifier scores into accurate multiclass probability estimates. In: KDD, pp. 694\u2013699. ACM (2002)","DOI":"10.1145\/775047.775151"},{"key":"6_CR38","unstructured":"Zhong, W., Kwok, J.T.: Accurate probability calibration for multiple classifiers. In: IJCAI, pp. 1939\u20131945 (2013)"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-16148-4_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T12:25:15Z","timestamp":1709814315000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-16148-4_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030161477","9783030161484"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-16148-4_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"22 March 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Macau","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 April 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 April 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.pakdd2019.org","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":"Microsoft Conf. Man. Toolkit CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"542","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":"137","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":"25% - 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":"3.79","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":"5.85","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"In addition, there were 31 PAKDD 2019 Workshops' papers accepted for publication","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}