Peter Flach
Peter Flach
Professor of Artificial Intelligence, Department of Computer Science, University of Bristol
Verified email at - Homepage
Cited by
Cited by
Machine learning: the art and science of algorithms that make sense of data
P Flach
Cambridge university press, 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
Multi-instance kernels
T Gärtner, PA Flach, A Kowalczyk, AJ Smola
ICML 2 (3), 7, 2002
Rule evaluation measures: A unifying view
N Lavrač, P Flach, B Zupan
International conference on inductive logic programming, 174-185, 1999
Subgroup discovery with CN2-SD
N Lavrač, B Kavšek, P Flach, L Todorovski
Journal of Machine Learning Research 5 (Feb), 153-188, 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
Learning decision trees using the area under the ROC curve
C Ferri, P Flach, J Hernández-Orallo
Icml 2, 139-146, 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
Precision-recall-gain curves: PR analysis done right
P Flach, M Kull
Advances in neural information processing systems 28, 2015
Propositionalization approaches to relational data mining
S Kramer, N Lavrač, P Flach
Relational data mining, 262-291, 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
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
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
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
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
Roc ‘n’rule learning—towards a better understanding of covering algorithms
J Fürnkranz, PA Flach
Machine learning 58, 39-77, 2005
Abduction and Induction: Essays on their relation and integration
PA Flach, AC Kakas
Kluwer Academic Publishers, 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
Database dependency discovery: a machine learning approach
PA Flach, I Savnik
AI communications 12 (3), 139-160, 1999
Naive Bayesian classification of structured data
PA Flach, N Lachiche
Machine learning 57, 233-269, 2004
The system can't perform the operation now. Try again later.
Articles 1–20