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D-Index & Metrics

Computer Science

D-Index
76
Citations
39035
World Ranking
1314
National Ranking
75

Overview

Peter A. Flach is affiliated with the University of Bristol in the United Kingdom. Their research primarily focuses on the field of Computer Science, with significant contributions in Artificial Intelligence and related subfields.

The scientist's work spans several subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems and Management, Signal Processing, and Surgery. Their research topics cover Explainable Artificial Intelligence (XAI), Adversarial Robustness in Machine Learning, Machine Learning and Data Classification, Anomaly Detection Techniques and Applications, Context-Aware Activity Recognition Systems, Data Stream Mining Techniques, and Scientific Computing and Data Management.

Frequent coauthors collaborating with Peter A. Flach include Raúl Santos-Rodríguez, Kacper Sokol, Miquel Perelló-Nieto, Taku Yamagata, and Emma L. Tonkin.

They have published extensively in several venues. The most frequent publication venues include arXiv (Cornell University), which accounts for 19 publications, Machine Learning with 2 publications, as well as venues such as Zenodo (CERN European Organization for Nuclear Research), KI - Künstliche Intelligenz, and the IEEE Journal of Biomedical and Health Informatics.

Notable recent papers authored or coauthored by Peter A. Flach are:

  • One Explanation Does Not Fit All, 2020, KI - Künstliche Intelligenz
  • Classifier calibration: a survey on how to assess and improve predicted class probabilities, 2023, Machine Learning
  • Human Activity Recognition Based on Dynamic Active Learning, 2020, IEEE Journal of Biomedical and Health Informatics
  • FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems, 2020, The Journal of Open Source Software
  • One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency, 2020, arXiv (Cornell University)

Best Publications

  • Advances in Neural Information Processing Systems 28

    Peter A Flach;Meelis Kull

  • Machine Learning: The Art and Science of Algorithms that Make Sense of Data

    Peter Flach

  • Evaluation Measures for Multi-class Subgroup Discovery

    Tarek Abudawood;Peter Flach

  • On Graph Kernels: Hardness Results and Efficient Alternatives

    Thomas Gärtner;Thomas Gärtner;Peter A. Flach;Stefan Wrobel

  • Multi-Instance Kernels

    Thomas Gärtner;Peter A. Flach;Adam Kowalczyk;Alex J. Smola

  • Rule Evaluation Measures: A Unifying View

    Nada Lavrac;Peter A. Flach;Blaz Zupan

  • Subgroup Discovery with CN2-SD

    Nada Lavrač;Branko Kavšek;Peter Flach;Ljupčo Todorovski

  • FACE: Feasible and Actionable Counterfactual Explanations

    Rafael Poyiadzi;Kacper Sokol;Raul Santos-Rodriguez;Tijl De Bie

  • Abduction and Induction

    Peter A. Flach;Antonis C. Kakas

  • Proceedings of the 28th International Conference on Machine Learning

    José Hernández-Orallo;Peter A Flach;Cesar Ferri

  • Propositionalization approaches to relational data mining

    Stefan Kramer;Nada Lavrač;Peter Flach

  • Learning Decision Trees Using the Area Under the ROC Curve

    César Ferri;Peter A. Flach;José Hernández-Orallo

  • CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories

    Fernando Martinez-Plumed;Lidia Contreras-Ochando;Cesar Ferri;Jose Hernandez-Orallo

  • The geometry of ROC space: understanding machine learning metrics through ROC isometrics

    Peter A. Flach

  • Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence

    Yu Chen;Tom Diethe;Peter A Flach

  • Precision-Recall-Gain curves: PR analysis done right

    Peter A. Flach;Meelis Kull

  • ROC 'n' rule learning: towards a better understanding of covering algorithms

    Johannes Fürnkranz;Peter A. Flach

  • Abduction and induction: essays on their relation and integration

    Peter A Flach;Antonis C Kakas

  • Bridging e-Health and the Internet of Things: The SPHERE Project

    Ni Zhu;Tom Diethe;Massimo Camplani;Lili Tao

  • Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS 2017)

    Meelis Kull;Telmo De Menezes E Silva Filho;Peter A Flach

  • Improved Dataset Characterisation for Meta-learning

    Yonghong Peng;Peter A. Flach;Carlos Soares;Pavel Brazdil

  • Machine Learning: ECML 2001

    Luc De Raedt;Peter Flach

  • Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration

    Meelis Kull;Miquel Perello Nieto;Markus Kängsepp;Telmo Silva Filho

Frequent Co-Authors

José Hernández-Orallo
José Hernández-Orallo Universitat Politècnica de València
Nada Lavrač
Nada Lavrač Jozef Stefan Institute
Antonis C. Kakas
Antonis C. Kakas University of Cyprus
Ian J Craddock
Ian J Craddock University of Bristol
Tijl De Bie
Tijl De Bie Ghent University
Shaomin Wu
Shaomin Wu University of Kent
Stan Matwin
Stan Matwin Dalhousie University
Luc De Raedt
Luc De Raedt KU Leuven
Johannes Fürnkranz
Johannes Fürnkranz Johannes Kepler University of Linz
Debbie A. Lawlor
Debbie A. Lawlor University of Bristol

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