Peter Flach

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Meelis KullProfessor of Artificial Intelligence, University of TartuEmail confirmado em ut.ee
José Hernández-OralloVRAIN (UPV), ValGRAI, CFI, CSER.Email confirmado em upv.es
Tom DietheAstraZeneca; University of BristolEmail confirmado em bristol.ac.uk
Cèsar FerriVRAIN (UPV), ValGRAIEmail confirmado em dsic.upv.es
Kacper SokolETH ZürichEmail confirmado em inf.ethz.ch
Nicolas LachicheProfessor in Computer Science, University of StrasbourgEmail confirmado em unistra.fr
Raúl Santos-RodríguezUniversity of BristolEmail confirmado em bristol.ac.uk
Niall TwomeyAmazon; University of Bristol (honorary)Email confirmado em bristol.ac.uk
Thomas GärtnerTU Wien (Technical University of Vienna)Email confirmado em tuwien.ac.at
Miquel Perelló-NietoResearch Associate at University of BristolEmail confirmado em bristol.ac.uk
Hao SongAmazon; University of BristolEmail confirmado em amazon.com
Antonis KakasUniversity of CyprusEmail confirmado em ucy.ac.cy
Ian CraddockUniversity of BristolEmail confirmado em bristol.ac.uk
Stefan WrobelFraunhofer IAIS and University of BonnEmail confirmado em iais.fraunhofer.de
Telmo de Menezes e Silva FilhoSenior Lecturer in Data Science, School of Engineering Maths and Technology, University of BristolEmail confirmado em bristol.ac.uk
Simon PriceResearch Fellow, University of BristolEmail confirmado em bristol.ac.uk
Tijl De BieFull Professor, Ghent UniversityEmail confirmado em ugent.be
Fernando Martínez-PlumedVRAIN, Valencian Research Institute for Artificial Intelligence, Universitat Politecnica de ValenciaEmail confirmado em dsic.upv.es
Shaomin WuProfessor of Business/Applied Statistics, Kent Business School, University of KentEmail confirmado em kent.ac.uk
Louise AC MillardUniversity of BristolEmail confirmado em bristol.ac.uk

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Peter Flach

Professor of Artificial Intelligence, Department of Computer Science, University of Bristol

Email confirmado em bristol.ac.uk - Página inicial

Artificial Intelligence Data Science Machine Learning


Título Ordenar por citações Ordenar por ano Ordenar por título	Citado por Citado por	Ano
Machine learning: the art and science of algorithms that make sense of data P Flach Cambridge university press, 2012	2014	2012
On graph kernels: Hardness results and efficient alternatives T Gärtner, P Flach, S Wrobel Learning Theory and Kernel Machines: 16th Annual Conference on Learning …, 2003	1304	2003
Multi-instance kernels T Gärtner, PA Flach, A Kowalczyk, AJ Smola ICML 2 (3), 7, 2002	733	2002
Rule evaluation measures: A unifying view N Lavrač, P Flach, B Zupan International Conference on Inductive Logic Programming, 174-185, 1999	604	1999
CRISP-DM twenty years later: From data mining processes to data science trajectories F Martínez-Plumed, L Contreras-Ochando, C Ferri, J Hernández-Orallo, ... IEEE transactions on knowledge and data engineering 33 (8), 3048-3061, 2019	600	2019
Subgroup discovery with CN2-SD N Lavrač, B Kavšek, P Flach, L Todorovski Journal of Machine Learning Research 5 (Feb), 153-188, 2004	573	2004
Precision-recall-gain curves: PR analysis done right P Flach, M Kull Advances in neural information processing systems 28, 2015	494	2015
Learning decision trees using the area under the ROC curve C Ferri, P Flach, J Hernández-Orallo Icml 2, 139-146, 2002	441	2002
The geometry of ROC space: understanding machine learning metrics through ROC isometrics PA Flach Proceedings of the 20th international conference on machine learning (ICML …, 2003	439	2003
FACE: feasible and actionable counterfactual explanations R Poyiadzi, K Sokol, R Santos-Rodriguez, T De Bie, P Flach Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 344-350, 2020	434	2020
Propositionalization approaches to relational data mining S Kramer, N Lavrač, P Flach Relational data mining, 262-291, 2001	418	2001
Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with dirichlet calibration M Kull, M Perello Nieto, M Kängsepp, T Silva Filho, H Song, P Flach Advances in neural information processing systems 32, 2019	412	2019
Explainability fact sheets: A framework for systematic assessment of explainable approaches K Sokol, P Flach Proceedings of the 2020 conference on fairness, accountability, and …, 2020	407	2020
A coherent interpretation of AUC as a measure of aggregated classification performance PA Flach, J Hernández-Orallo, C Ferri Proceedings of the 28th International Conference on Machine Learning (ICML …, 2011	362	2011
Bridging e-health and the internet of things: The sphere project N Zhu, T Diethe, M Camplani, L Tao, A Burrows, N Twomey, D Kaleshi, ... IEEE Intelligent Systems 30 (4), 39-46, 2015	335	2015
Roc ‘n’rule learning—towards a better understanding of covering algorithms J Fürnkranz, PA Flach Machine learning 58, 39-77, 2005	327	2005
Abduction and Induction: Essays on their relation and integration PA Flach, AC Kakas Kluwer Academic Publishers, 2000	296	2000
A unified view of performance metrics: Translating threshold choice into expected classification loss J Hernández-Orallo, P Flach, C Ferri The Journal of Machine Learning Research 13 (1), 2813-2869, 2012	284	2012
Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers M Kull, T Silva Filho, P Flach Artificial intelligence and statistics, 623-631, 2017	245	2017
Database dependency discovery: a machine learning approach PA Flach, I Savnik AI communications 12 (3), 139-160, 1999	241	1999

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