Peter Flach

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

Peter Flach

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

Verified email at bristol.ac.uk - Homepage

Artificial Intelligence Data Science Machine Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Machine learning: the art and science of algorithms that make sense of data P Flach Cambridge university press, 2012	1952	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	1280	2003
Multi-instance kernels T Gärtner, PA Flach, A Kowalczyk, AJ Smola ICML 2 (3), 7, 2002	706	2002
Rule evaluation measures: A unifying view N Lavrač, P Flach, B Zupan International conference on inductive logic programming, 174-185, 1999	602	1999
Subgroup discovery with CN2-SD N Lavrač, B Kavšek, P Flach, L Todorovski Journal of Machine Learning Research 5 (Feb), 153-188, 2004	574	2004
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	524	2019
Precision-recall-gain curves: PR analysis done right P Flach, M Kull Advances in neural information processing systems 28, 2015	447	2015
Learning decision trees using the area under the ROC curve C Ferri, P Flach, J Hernández-Orallo Icml 2, 139-146, 2002	442	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	431	2003
Propositionalization approaches to relational data mining S Kramer, N Lavrač, P Flach Relational data mining, 262-291, 2001	414	2001
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	400	2020
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	367	2020
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	361	2019
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	346	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	324	2015
Roc ‘n’rule learning—towards a better understanding of covering algorithms J Fürnkranz, PA Flach Machine learning 58, 39-77, 2005	321	2005
Abduction and Induction: Essays on their relation and integration PA Flach, AC Kakas Kluwer Academic Publishers, 2000	282	2000
A unified view of performance metrics: Translating threshold choice into expected classification loss J Hernández-Orallo, P Flach, C Ferri Ramírez Journal of Machine Learning Research 13, 2813-2869, 2012	272	2012
Database dependency discovery: a machine learning approach PA Flach, I Savnik AI communications 12 (3), 139-160, 1999	237	1999
Naive Bayesian classification of structured data PA Flach, N Lachiche Machine learning 57, 233-269, 2004	226	2004

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