Fault Forecasting Using Data-Driven Modeling: A Case Study for Metro do Porto Data Set

AuthID
P-00X-T4P
4
Author(s)
41
Editor(s)
Koprinska,I;Mignone,P;Guidotti,R;Jaroszewicz,S;Froning,H;Gullo,F;Ferreira,PM;Roqueiro,D;Ceddia,G;Nowaczyk,S;Gama,J;Ribeiro,R;Gavalda,R;Masciari,E;Ras,Z;Ritacco,E;Naretto,F;Theissler,A;Biecek,P;Verbeke,W;Schiele,G;Pernkopf,F;Blott,M;Bordino,I;Danesi,IL;Ponti,G;Severini,L;Appice,A;Andresini,G;Medeiros,I;Graca,G;Cooper,L;Ghazaleh,N;Richiardi,J;Saldana,D;Sechidis,K;Canakoglu,A;Pido,S;Pinoli,P;Bifet,A;Pashami,S
Document Type
Proceedings Paper
Year published
2023
Published
in MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT II in Communications in Computer and Information Science, ISSN: 1865-0929
Volume: 1753, Pages: 400-409 (10)
Conference
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (Ecml Pkdd), Date: SEP 19-23, 2022, Location: Grenoble, FRANCE
Indexing
Publication Identifiers
Dblp: conf/pkdd/DavariVR022
Scopus: 2-s2.0-85149945427
Wos: WOS:000967761200026
Source Identifiers
ISSN: 1865-0929
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