{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T23:11:21Z","timestamp":1744153881558,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030687793"},{"type":"electronic","value":"9783030687809"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","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":[[2021]]},"DOI":"10.1007\/978-3-030-68780-9_58","type":"book-chapter","created":{"date-parts":[[2021,2,24]],"date-time":"2021-02-24T17:04:13Z","timestamp":1614186253000},"page":"773-787","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Introducing Bidirectional Ordinal Classifier Cascades Based on a Pain Intensity Recognition Scenario"],"prefix":"10.1007","author":[{"given":"Peter","family":"Bellmann","sequence":"first","affiliation":[]},{"given":"Ludwig","family":"Lausser","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4759-5254","authenticated-orcid":false,"given":"Hans A.","family":"Kestler","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5118-0812","authenticated-orcid":false,"given":"Friedhelm","family":"Schwenker","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,2,25]]},"reference":[{"key":"58_CR1","series-title":"Advances in Pattern Recognition","doi-asserted-by":"publisher","DOI":"10.1007\/1-84628-219-5","volume-title":"Support Vector Machines for Pattern Classification","author":"S Abe","year":"2005","unstructured":"Abe, S.: Support Vector Machines for Pattern Classification. Advances in Pattern Recognition. Springer, London (2005). https:\/\/doi.org\/10.1007\/1-84628-219-5"},{"key":"58_CR2","first-page":"113","volume":"1","author":"EL Allwein","year":"2000","unstructured":"Allwein, E.L., Schapire, R.E., Singer, Y.: Reducing multiclass to binary: a unifying approach for margin classifiers. J. Mach. Learn. Res. 1, 113\u2013141 (2000)","journal-title":"J. Mach. Learn. Res."},{"doi-asserted-by":"crossref","unstructured":"Bellmann, P., Thiam, P., Schwenker, F.: Dominant channel fusion architectures - an intelligent late fusion approach. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138 (2020)","key":"58_CR3","DOI":"10.1109\/IJCNN48605.2020.9206814"},{"doi-asserted-by":"crossref","unstructured":"Bellmann, P., Hihn, H., Braun, D., Schwenker, F.: Binary classification: counterbalancing class imbalance by applying regression models in combination with one-sided label shifts. In: ICAART. SCITEPRESS (2021, to be published)","key":"58_CR4","DOI":"10.5220\/0010236307240731"},{"key":"58_CR5","doi-asserted-by":"publisher","first-page":"164380","DOI":"10.1109\/ACCESS.2020.3021596","volume":"8","author":"P Bellmann","year":"2020","unstructured":"Bellmann, P., Schwenker, F.: Ordinal classification: working definition and detection of ordinal structures. IEEE Access 8, 164380\u2013164391 (2020)","journal-title":"IEEE Access"},{"key":"58_CR6","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/978-3-030-58309-5_12","volume-title":"Artificial Neural Networks in Pattern Recognition","author":"P Bellmann","year":"2020","unstructured":"Bellmann, P., Thiam, P., Schwenker, F.: Pain intensity recognition - an analysis of short-time sequences in a real-world scenario. In: Schilling, F.-P., Stadelmann, T. (eds.) ANNPR 2020. LNCS (LNAI), vol. 12294, pp. 149\u2013161. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58309-5_12"},{"issue":"5\u20136","key":"58_CR7","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1016\/j.neunet.2005.06.023","volume":"18","author":"JS Cardoso","year":"2005","unstructured":"Cardoso, J.S., da Costa, J.F.P., Cardoso, M.J.: Modelling ordinal relations with SVMs: an application to objective aesthetic evaluation of breast cancer conservative treatment. Neural Netw. 18(5\u20136), 808\u2013817 (2005)","journal-title":"Neural Netw."},{"key":"58_CR8","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"doi-asserted-by":"crossref","unstructured":"Chu, W., Keerthi, S.S.: New approaches to support vector ordinal regression. In: ICML. ACM International Conference Proceeding Series, vol. 119, pp. 145\u2013152. ACM (2005)","key":"58_CR9","DOI":"10.1145\/1102351.1102370"},{"issue":"3","key":"58_CR10","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1162\/neco.2007.19.3.792","volume":"19","author":"W Chu","year":"2007","unstructured":"Chu, W., Keerthi, S.S.: Support vector ordinal regression. Neural Comput. 19(3), 792\u2013815 (2007)","journal-title":"Neural Comput."},{"unstructured":"Dietterich, T.G., Bakiri, G.: Error-correcting output codes: a general method for improving multiclass inductive learning programs. In: AAAI, pp. 572\u2013577. AAAI Press\/The MIT Press (1991)","key":"58_CR11"},{"issue":"3","key":"58_CR12","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1016\/j.patrec.2008.10.002","volume":"30","author":"S Escalera","year":"2009","unstructured":"Escalera, S., Pujol, O., Radeva, P.: Separability of ternary codes for sparse designs of error-correcting output codes. Pattern Recognit. Lett. 30(3), 285\u2013297 (2009)","journal-title":"Pattern Recognit. Lett."},{"issue":"1","key":"58_CR13","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1109\/TPAMI.2008.266","volume":"32","author":"S Escalera","year":"2010","unstructured":"Escalera, S., Pujol, O., Radeva, P.: On the decoding process in ternary error-correcting output codes. IEEE Trans. Pattern Anal. Mach. Intell. 32(1), 120\u2013134 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"58_CR14","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1109\/TKDE.2015.2457911","volume":"28","author":"PA Guti\u00e9rrez","year":"2016","unstructured":"Guti\u00e9rrez, P.A., P\u00e9rez-Ortiz, M., S\u00e1nchez-Monedero, J., Fern\u00e1ndez-Navarro, F., Herv\u00e1s-Mart\u00ednez, C.: Ordinal regression methods: survey and experimental study. IEEE Trans. Knowl. Data Eng. 28(1), 127\u2013146 (2016)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"3","key":"58_CR15","doi-asserted-by":"publisher","first-page":"2319","DOI":"10.1007\/s11063-020-10351-3","volume":"52","author":"H Hihn","year":"2020","unstructured":"Hihn, H., Braun, D.A.: Specialization in hierarchical learning systems. Neural Process. Lett. 52(3), 2319\u20132352 (2020). https:\/\/doi.org\/10.1007\/s11063-020-10351-3","journal-title":"Neural Process. Lett."},{"issue":"1","key":"58_CR16","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1504\/IJDMMM.2008.022537","volume":"1","author":"JC H\u00fchn","year":"2008","unstructured":"H\u00fchn, J.C., H\u00fcllermeier, E.: Is an ordinal class structure useful in classifier learning? IJDMMM 1(1), 45\u201367 (2008)","journal-title":"IJDMMM"},{"issue":"1","key":"58_CR17","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/s12530-016-9158-4","volume":"8","author":"M K\u00e4chele","year":"2016","unstructured":"K\u00e4chele, M., et al.: Adaptive confidence learning for the personalization of pain intensity estimation systems. Evolving Syst. 8(1), 71\u201383 (2016). https:\/\/doi.org\/10.1007\/s12530-016-9158-4","journal-title":"Evolving Syst."},{"issue":"5","key":"58_CR18","doi-asserted-by":"publisher","first-page":"854","DOI":"10.1109\/JSTSP.2016.2535962","volume":"10","author":"M K\u00e4chele","year":"2016","unstructured":"K\u00e4chele, M., Thiam, P., Amirian, M., Schwenker, F., Palm, G.: Methods for person-centered continuous pain intensity assessment from bio-physiological channels. J. Sel. Top. Signal Process. 10(5), 854\u2013864 (2016)","journal-title":"J. Sel. Top. Signal Process."},{"key":"58_CR19","doi-asserted-by":"crossref","DOI":"10.1002\/9781118914564","volume-title":"Combining Pattern Classifiers: Methods and Algorithms","author":"LI Kuncheva","year":"2014","unstructured":"Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley, Hoboken (2014)"},{"key":"58_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1007\/978-3-319-20248-8_9","volume-title":"Multiple Classifier Systems","author":"R Lattke","year":"2015","unstructured":"Lattke, R., Lausser, L., M\u00fcssel, C., Kestler, H.A.: Detecting ordinal class structures. In: Schwenker, F., Roli, F., Kittler, J. (eds.) MCS 2015. LNCS, vol. 9132, pp. 100\u2013111. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-20248-8_9"},{"issue":"3","key":"58_CR21","doi-asserted-by":"publisher","first-page":"2583","DOI":"10.1007\/s11063-020-10362-0","volume":"52","author":"L Lausser","year":"2020","unstructured":"Lausser, L., Sch\u00e4fer, L.M., K\u00fchlwein, S.D., Kestler, A.M.R., Kestler, H.A.: Detecting ordinal subcascades. Neural Process. Lett. 52(3), 2583\u20132605 (2020). https:\/\/doi.org\/10.1007\/s11063-020-10362-0","journal-title":"Neural Process. Lett."},{"issue":"1","key":"58_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-48150-z","volume":"9","author":"L Lausser","year":"2019","unstructured":"Lausser, L., Sch\u00e4fer, L.M., Schirra, L.R., Szekely, R., Schmid, F., Kestler, H.A.: Assessing phenotype order in molecular data. Sci. Rep. 9(1), 1\u201310 (2019)","journal-title":"Sci. Rep."},{"issue":"17","key":"58_CR23","doi-asserted-by":"publisher","first-page":"5984","DOI":"10.3390\/app10175984","volume":"10","author":"RM Al-Eidan","year":"2020","unstructured":"Al-Eidan, R.M., Al-Khalifa, H., Al-Salman, A.: Deep-learning-based models for pain recognition: a systematic review. Appl. Sci. 10(17), 5984 (2020)","journal-title":"Appl. Sci."},{"issue":"3","key":"58_CR24","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/MCAS.2006.1688199","volume":"6","author":"R Polikar","year":"2006","unstructured":"Polikar, R.: Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 6(3), 21\u201345 (2006). https:\/\/doi.org\/10.1109\/MCAS.2006.1688199","journal-title":"IEEE Circuits Syst. Mag."},{"key":"58_CR25","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1007\/978-3-030-58309-5_11","volume-title":"Artificial Neural Networks in Pattern Recognition","author":"T Ricken","year":"2020","unstructured":"Ricken, T., Steinert, A., Bellmann, P., Walter, S., Schwenker, F.: Feature extraction: a time window analysis based on the X-ITE pain database. In: Schilling, F.-P., Stadelmann, T. (eds.) ANNPR 2020. LNCS (LNAI), vol. 12294, pp. 138\u2013148. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58309-5_11"},{"key":"58_CR26","first-page":"17","volume":"6","author":"F Schwenker","year":"2006","unstructured":"Schwenker, F., Dietrich, C., Thiel, C., Palm, G.: Learning of decision fusion mappings for pattern recognition. Int. J. Artif. Intell. Mach. Learn. (AIML) 6, 17\u201321 (2006)","journal-title":"Int. J. Artif. Intell. Mach. Learn. (AIML)"},{"doi-asserted-by":"crossref","unstructured":"Snoek, C., Worring, M., Smeulders, A.W.M.: Early versus late fusion in semantic video analysis. In: ACM Multimedia, pp. 399\u2013402. ACM (2005)","key":"58_CR27","DOI":"10.1145\/1101149.1101236"},{"doi-asserted-by":"crossref","unstructured":"Tax, D.M.J., Duin, R.P.W.: Using two-class classifiers for multiclass classification. In: ICPR, vol. 2, pp. 124\u2013127. IEEE Computer Society (2002)","key":"58_CR28","DOI":"10.1109\/ICPR.2002.1048253"},{"doi-asserted-by":"publisher","unstructured":"Thiam, P., et al.: Multi-modal pain intensity recognition based on the senseemotion database. IEEE Trans. Affect. Comput. 1 (2019). https:\/\/doi.org\/10.1109\/taffc.2019.2892090","key":"58_CR29","DOI":"10.1109\/taffc.2019.2892090"},{"issue":"20","key":"58_CR30","doi-asserted-by":"publisher","first-page":"4503","DOI":"10.3390\/s19204503","volume":"19","author":"P Thiam","year":"2019","unstructured":"Thiam, P., Bellmann, P., Kestler, H.A., Schwenker, F.: Exploring deep physiological models for nociceptive pain recognition. Sensors 19(20), 4503 (2019)","journal-title":"Sensors"},{"issue":"3","key":"58_CR31","doi-asserted-by":"publisher","first-page":"839","DOI":"10.3390\/s20030839","volume":"20","author":"P Thiam","year":"2020","unstructured":"Thiam, P., Kestler, H.A., Schwenker, F.: Two-stream attention network for pain recognition from video sequences. Sensors 20(3), 839 (2020)","journal-title":"Sensors"},{"key":"58_CR32","volume-title":"The Nature of Statistical Learning Theory","author":"V Vapnik","year":"2013","unstructured":"Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (2013)"},{"doi-asserted-by":"publisher","unstructured":"Walter, S., et al.: The biovid heat pain database data for the advancement and systematic validation of an automated pain recognition system. In: CYBCONF, pp. 128\u2013131. IEEE (2013). https:\/\/doi.org\/10.1109\/CYBConf.2013.6617456","key":"58_CR33","DOI":"10.1109\/CYBConf.2013.6617456"},{"doi-asserted-by":"publisher","unstructured":"Werner, P., Lopez-Martinez, D., Walter, S., Al-Hamadi, A., Gruss, S., Picard, R.: Automatic recognition methods supporting pain assessment: a survey. IEEE Trans. Affect. Comput. 1 (2019). https:\/\/doi.org\/10.1109\/taffc.2019.2946774","key":"58_CR34","DOI":"10.1109\/taffc.2019.2946774"},{"issue":"6","key":"58_CR35","doi-asserted-by":"publisher","first-page":"80","DOI":"10.2307\/3001968","volume":"1","author":"F Wilcoxon","year":"1945","unstructured":"Wilcoxon, F.: Individual comparisons by ranking methods. Biom. Bull. 1(6), 80\u201383 (1945)","journal-title":"Biom. Bull."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition. ICPR International Workshops and Challenges"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-68780-9_58","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T20:11:46Z","timestamp":1724530306000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-68780-9_58"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030687793","9783030687809"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-68780-9_58","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"25 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 January 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 January 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ICPR2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.icpr2020.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}