{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:46:53Z","timestamp":1755222413052,"version":"3.43.0"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030840594"},{"type":"electronic","value":"9783030840600"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-84060-0_10","type":"book-chapter","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T06:09:49Z","timestamp":1628662189000},"page":"153-171","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Reliable AI Through SVDD and Rule Extraction"],"prefix":"10.1007","author":[{"given":"Alberto","family":"Carlevaro","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maurizio","family":"Mongelli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,10]]},"reference":[{"key":"10_CR1","series-title":"Advances in Pattern Recognition","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-84996-098-4","volume-title":"Support Vector Machines for Pattern Classification","author":"S Abe","year":"2010","unstructured":"Abe, S.: Support Vector Machines for Pattern Classification. Advances in Pattern Recognition, 2nd edn. Springer, London (2010). https:\/\/doi.org\/10.1007\/978-1-84996-098-4","edition":"2"},{"issue":"2","key":"10_CR2","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1109\/69.842268","volume":"12","author":"E Boros","year":"2000","unstructured":"Boros, E., Hammer, P.L., Ibaraki, T., et al.: An implementation of logical analysis of data. IEEE Trans. Knowl. Data Eng. 12(2), 292\u2013306 (2000)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"1\u20133","key":"10_CR3","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.neucom.2010.02.016","volume":"74","author":"N Barakat","year":"2010","unstructured":"Barakat, N., Bradley, A.P.: Rule extraction from support vector machines: a review. Neurocomputing 74(1\u20133), 178\u2013190 (2010). https:\/\/doi.org\/10.1016\/j.neucom.2010.02.016. ISSN 0925\u20132312","journal-title":"Neurocomputing"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Campagner, A., Cabitza, F., Ciucci, D.: Three-way decision for handling uncertainty in machine learning: a narrative review. In: International Joint Conference on Rough Sets (2020)","DOI":"10.1007\/978-3-030-52705-1_10"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Balasubramanian, V.N., Ho, S.S., Vovk, V.: Conformal Prediction for Reliable Machine Learning, 1st edn. Morgan Kaufmann Elsevier, Waltham (2014). ISBN 9780123985378","DOI":"10.1016\/B978-0-12-398537-8.00003-1"},{"key":"10_CR6","unstructured":"Chaudhuri, A., et al.: Sampling method for fast training of support vector data description. arXiv e-prints, 2016arXiv160605382C (2006)"},{"key":"10_CR7","unstructured":"European Union Aviation Safety Angency: Concepts of Design Assurance for Neural Networks CoDANN. 2020 mar, EASA AI Task Force. Daedalean, AG. https:\/\/www.easa.europa.eu\/sites\/default\/files\/dfu\/EASA-DDLN-Concepts-of-Design-Assurance-for-Neural-Networks-CoDANN.pdf"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Fisch, D., Hofmann, A., Sick, B.: On the versatility of radial basis function neural networks: a case study in the field of intrusion detection. Inf. Sci. 180(12), 2421\u20132439 (2010). http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0020025510001015","DOI":"10.1016\/j.ins.2010.02.023"},{"key":"10_CR9","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.trc.2014.04.014","volume":"46","author":"JI Ge","year":"2014","unstructured":"Ge, J.I., Orosz, G.: Dynamics of connected vehicle systems with delayed acceleration feedback. Transp. Res. C Emerg. Technol. 46, 46\u201364 (2014). Cited By 90","journal-title":"Transp. Res. C Emerg. Technol."},{"key":"10_CR10","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1016\/j.patcog.2010.08.025","volume":"44","author":"G Huang","year":"2011","unstructured":"Huang, G., Chen, H., Zhou, Z., Yin, F., Guo, K.: Two-class support vector data description. Pattern Recogn. 44, 320\u2013329 (2011)","journal-title":"Pattern Recogn."},{"issue":"1","key":"10_CR11","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1109\/COMST.2015.2410831","volume":"18","author":"D Jia","year":"2016","unstructured":"Jia, D., Lu, K., Wang, J., et al.: A survey on platoon-based vehicular cyber-physical systems. IEEE Commun. Surv. Tutor. 18(1), 263\u2013284 (2016)","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"10_CR12","unstructured":"Jones, C.A.: Lecture notes: Math2640 introduction to optimisation 4. University of Leeds, School of Mathematics, Technical report (2005)"},{"key":"10_CR13","doi-asserted-by":"publisher","unstructured":"Mongelli, M., Muselli, M., Scorzoni, A., Ferrari, E.: Accellerating PRISM validation of vehicle platooning through machine learning, pp. 452\u2013456 (2019). https:\/\/doi.org\/10.1109\/ICSRS48664.2019.8987672","DOI":"10.1109\/ICSRS48664.2019.8987672"},{"key":"10_CR14","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1049\/iet-cps.2018.5055","volume":"4","author":"M Mongelli","year":"2018","unstructured":"Mongelli, M., Muselli, M., Ferrari, E., Fermi, A.: Performance validation of vehicle platooning via intelligible analytics. IET Cyber-Phys. Syst.: Theory Appl. 4, 120\u2013127 (2018). https:\/\/doi.org\/10.1049\/iet-cps.2018.5055","journal-title":"IET Cyber-Phys. Syst.: Theory Appl."},{"key":"10_CR15","doi-asserted-by":"publisher","unstructured":"Fermi, A., Mongelli, M., Muselli, M., Ferrari, E.: Identification of safety regions in vehicle platooning via machine learning. In: 2018 14th IEEE International Workshop on Factory Communication Systems (WFCS), Imperia, Italy, pp. 1\u20134 (2018). https:\/\/doi.org\/10.1109\/WFCS.2018.8402372","DOI":"10.1109\/WFCS.2018.8402372"},{"issue":"1","key":"10_CR16","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1109\/TKDE.2009.206","volume":"23","author":"M Muselli","year":"2011","unstructured":"Muselli, M., Ferrari, E.: Coupling logical analysis of data and shadow clustering for partially defined positive Boolean function reconstruction. IEEE Trans. Knowl. Data Eng. 23(1), 37\u201350 (2011)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11063-006-9007-8","volume":"24","author":"H Nunez","year":"2006","unstructured":"Nunez, H., Angulo, C., Catal\u00e0, A.: Rule-based learning systems for support vector machines. Neural Process. Lett. 24, 1\u201318 (2006)","journal-title":"Neural Process. Lett."},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Oncu, S., van de Wouw, N., Nijmeijer, H.: Cooperative adaptive cruise control: tradeoffs between control and network specifications. In: 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), Washington, DC, USA, pp. 2051\u20132056 (2011)","DOI":"10.1109\/ITSC.2011.6082894"},{"key":"10_CR19","unstructured":"KEEL: Website: KEEL (Knowledge Extraction based on Evolutionary Learning), November 2012. http:\/\/sci2s.ugr.es\/keel\/datasets.php"},{"key":"10_CR20","unstructured":"Kools, J.: 6 functions for generating artificial datasets. https:\/\/www.mathworks.com\/matlabcentral\/fileexchange\/41459-6-functions-for-generating-artificial-datasets. MATLAB Central File Exchange. Accessed 4 Apr 2021"},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Pop, P., Scholle, D., Hansson, H., et al.: The safecopecsel project: safe cooperating cyber-physical systems using wireless communication. In: 2016 Euromicro Conference on Digital System Design (DSD), Limassol, Cyprus, pp. 532\u2013538 (2016)","DOI":"10.1109\/DSD.2016.25"},{"key":"10_CR22","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.micpro.2017.07.003","volume":"53","author":"P Pop","year":"2017","unstructured":"Pop, P., Scholle, D., Sljivo, I., et al.: Safe cooperating cyber-physical systems using wireless communication. Microprocess. Microsyst. 53, 42\u201350 (2017)","journal-title":"Microprocess. Microsyst."},{"key":"10_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1007\/978-3-319-99229-7_37","volume-title":"Computer Safety, Reliability, and Security","author":"K Czarnecki","year":"2018","unstructured":"Czarnecki, K., Salay, R.: Towards a framework to manage perceptual uncertainty for safe automated driving. In: Gallina, B., Skavhaug, A., Schoitsch, E., Bitsch, F. (eds.) SAFECOMP 2018. LNCS, vol. 11094, pp. 439\u2013445. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-99229-7_37"},{"issue":"3","key":"10_CR24","doi-asserted-by":"publisher","first-page":"1985","DOI":"10.1109\/TVT.2016.2585018","volume":"66","author":"S Santini","year":"2017","unstructured":"Santini, S., Salvi, A., Valente, A.S., et al.: A consensus-based approach for platooning with intervehicular communications and its validation in realistic scenarios. IEEE Trans. Veh. Technol. 66(3), 1985\u20131999 (2017)","journal-title":"IEEE Trans. Veh. Technol."},{"key":"10_CR25","unstructured":"Standardization in the area of Artificial Intelligence, ISO\/IEC. Creation date 2017, Washington, DC 20036, USA (2017). https:\/\/www.iso.org\/committee\/6794475.html"},{"issue":"9","key":"10_CR26","doi-asserted-by":"publisher","first-page":"4150","DOI":"10.1109\/TVT.2013.2277802","volume":"62","author":"M Segata","year":"2013","unstructured":"Segata, M., Cigno, R.L.: Automatic emergency braking: realistic analysis of car dynamics and network performance. IEEE Trans. Veh. Technol. 62(9), 4150\u20134161 (2013)","journal-title":"IEEE Trans. Veh. Technol."},{"key":"10_CR27","unstructured":"Road vehicles Safety of the intended functionality PD ISO PAS 21448:2019. International Organization for Standardization, Geneva, CH"},{"key":"10_CR28","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.1016\/S0167-8655(99)00087-2","volume":"20","author":"DMJ Tax","year":"1999","unstructured":"Tax, D.M.J., Duin, R.P.W.: Support vector domain description. Pattern Recogn. Lett. 20, 1191\u20131199 (1999)","journal-title":"Pattern Recogn. Lett."},{"key":"10_CR29","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1023\/B:MACH.0000008084.60811.49","volume":"54","author":"DMJ Tax","year":"2004","unstructured":"Tax, D.M.J., Duin, R.P.W.: Support vector data description. Mach. Learn. 54, 45\u201366 (2004)","journal-title":"Mach. Learn."},{"key":"10_CR30","unstructured":"Tax, D.M.: One-class classification, concept-learning in the absence of counter-examples. Ph.D. dissertation, Delft University of Technology (2001)"},{"key":"10_CR31","unstructured":"Theissler, A., Dear, I.: Autonomously determining the parameters for SVDD with RBF kernel from a one-class training set. In: Conference: WASET International Conference on Machine Intelligence, Stockholm (2013)"},{"key":"10_CR32","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-2440-0","volume-title":"The Nature of Statistical Learning Theory","author":"V Vapnik","year":"1995","unstructured":"Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995). https:\/\/doi.org\/10.1007\/978-1-4757-2440-0"},{"key":"10_CR33","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1613\/jair.4439","volume":"52","author":"Y Wiener","year":"2015","unstructured":"Wiener, Y., El-Yaniv, R.: Agnostic pointwise-competitive selective classification. J. Artif. Intell. Res. 52, 171\u2013201 (2015)","journal-title":"J. Artif. Intell. Res."},{"issue":"9","key":"10_CR34","doi-asserted-by":"publisher","first-page":"4206","DOI":"10.1109\/TVT.2014.2311384","volume":"63","author":"L Xu","year":"2014","unstructured":"Xu, L., Wang, L.Y., Yin, G., et al.: Communication information structures and contents for enhanced safety of highway vehicle platoons. IEEE Trans. Veh. Technol. 63(9), 4206\u20134220 (2014)","journal-title":"IEEE Trans. Veh. Technol."},{"key":"10_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.knosys.2012.12.003","volume":"42","author":"P Zhu","year":"2012","unstructured":"Zhu, P., Hu, Q.: Rule extraction from support vector machines based on consistent region covering reduction. Knowl.-Based Syst. 42, 1\u20138 (2012)","journal-title":"Knowl.-Based Syst."}],"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\/978-3-030-84060-0_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,10]],"date-time":"2025-08-10T22:02:45Z","timestamp":1754863365000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-84060-0_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030840594","9783030840600"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-84060-0_10","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":"10 August 2021","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 August 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 August 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cd-make2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cd-make.net\/","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":"48","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":"20","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":"2","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":"42% - 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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}