Peter Flach

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Meelis KullAssociate Professor, University of TartuVerified email at ut.ee
Tom DietheAstraZeneca; University of BristolVerified email at bristol.ac.uk
José Hernández-OralloVRAIN, Valencian Research Institute for Artificial Intelligence, Universitat Politecnica de ValenciaVerified email at upv.es
César FerriVRAIN. Universitat Politècnica de ValènciaVerified email at dsic.upv.es
Niall TwomeyAmazon; University of Bristol (honorary)Verified email at bristol.ac.uk
Kacper SokolRMIT University; University of BristolVerified email at rmit.edu.au
Nicolas LachicheAssociate professor in Computer Science, University of StrasbourgVerified email at unistra.fr
Thomas GärtnerTU Wien (Technical University of Vienna)Verified email at tuwien.ac.at
Raúl Santos-RodríguezUniversity of BristolVerified email at bristol.ac.uk
Hao SongSeldon; University of BristolVerified email at bristol.ac.uk
Ian CraddockUniversity of BristolVerified email at bristol.ac.uk
Miquel Perelló-NietoPhD Candidate at University 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
Shaomin WuProfessor of Business/Applied Statistics, Kent Business School, University of KentVerified email at kent.ac.uk
Stefan WrobelFraunhofer IAIS and University of BonnVerified email at iais.fraunhofer.de
Tijl De BieFull Professor, Ghent UniversityVerified email at ugent.be
Louise Amanda Claire MillardUniversity of BristolVerified email at bristol.ac.uk
Johannes FürnkranzJohannes Kepler University, LinzVerified email at faw.jku.at
Simon RawlesUniversity of NottinghamVerified email at rawles.net

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	1585	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	1160	2003
Multi-instance kernels T Gärtner, PA Flach, A Kowalczyk, AJ Smola ICML 2 (3), 7, 2002	686	2002
Rule evaluation measures: A unifying view N Lavrač, P Flach, B Zupan Inductive Logic Programming: 9th International Workshop, ILP-99 Bled …, 1999	589	1999
Subgroup discovery with CN2-SD N Lavrač, B Kavšek, P Flach, L Todorovski Journal of Machine Learning Research 5 (Feb), 153-188, 2004	530	2004
Learning decision trees using the area under the ROC curve C Ferri, P Flach, J Hernández-Orallo Icml 2, 139-146, 2002	420	2002
Propositionalization approaches to relational data mining S Kramer, N Lavrač, P Flach Relational data mining, 262-291, 2001	409	2001
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	399	2003
Roc ‘n’rule learning—towards a better understanding of covering algorithms J Fürnkranz, PA Flach Machine learning 58, 39-77, 2005	313	2005
Precision-recall-gain curves: PR analysis done right P Flach, M Kull Advances in neural information processing systems 28, 2015	305	2015
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	284	2015
Abduction and Induction: Essays on their relation and integration PA Flach, AC Kakas Kluwer Academic Publishers, 2000	273*	2000
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	272	2011
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	234	2012
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	231	2019
Database dependency discovery: a machine learning approach PA Flach, I Savnik AI communications 12 (3), 139-160, 1999	222	1999
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	212	2020
Confirmation-guided discovery of first-order rules with Tertius PA Flach, N Lachiche Machine learning 42 (1-2), 61, 2001	210	2001
Kernels and distances for structured data T Gärtner, JW Lloyd, PA Flach Machine Learning 57, 205-232, 2004	208	2004
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	203	2020

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