Flach, Peter

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

Flach, P. (2012) · Cambridge University Press eBooks

Machine Learning and AlgorithmsData Mining and Machine Learning Applications

Cited by 753

FACE: Feasible and Actionable Counterfactual Explanations

Poyiadzi, R., Sokol, K., Santos-Rodriguez, R., De Bie, T., & Flach, P. (2020) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 317

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

Zhu, N., Diethe, T., Camplani, M., Tao, L., Burrows, A., Twomey, N., Kaleshi, D., Mirmehdi, M., Flach, P., & Craddock, I. (2015) · IEEE Intelligent Systems

Context-Aware Activity Recognition SystemsInnovation in Digital Healthcare Systems

Cited by 242

Classifier calibration: a survey on how to assess and improve predicted class probabilities

Silva Filho, T., Song, H., Perello-Nieto, M., Santos-Rodriguez, R., Kull, M., & Flach, P. (2023) · Machine Learning

Imbalanced Data Classification TechniquesMachine Learning and Data Classification

Cited by 228

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

Kull, M., Perello-Nieto, M., Kängsepp, M., Filho, T. S., Song, H., & Flach, P. (2019) · arXiv

Adversarial Robustness in Machine LearningMachine Learning and Data Classification

Cited by 206

One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency

Sokol, K., & Flach, P. (2020) · KI - Künstliche Intelligenz

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 186

A Comprehensive Study of Activity Recognition Using Accelerometers

Twomey, N., Diethe, T., Fafoutis, X., Elsts, A., McConville, R., Flach, P., & Craddock, I. (2018) · Informatics

Context-Aware Activity Recognition SystemsPhysical Activity and Health

Cited by 143

A Comprehensive Study of Activity Recognition Using Accelerometers

Twomey, N., Diethe, T., Fafoutis, X., Elsts, A., McConville, R., Flach, P., & Craddock, I. (2018) · Informatics

Context-Aware Activity Recognition SystemsPhysical Activity and Health

Cited by 143

Caveats and pitfalls of ROC analysis in clinical microarray research (and how to avoid them)

Berrar, D., & Flach, P. (2012) · Briefings in Bioinformatics

Gene expression and cancer classificationImbalanced Data Classification Techniques

Cited by 136

Confirmation-Guided Discovery of First-Order Rules with Tertius

Flach, P. A., & Lachiche, N. (2001) · Machine Learning

Logic, Reasoning, and KnowledgeData Mining Algorithms and Applications

Cited by 131

ILP turns 20: Biography and future challenges

Muggleton, S., De Raedt, L., Poole, D., Bratko, I., Flach, P., Inoue, K., & Srinivasan, A. (2012) · Machine Learning

Logic, Reasoning, and KnowledgeData Mining Algorithms and Applications

Cited by 126

Beyond sigmoids: How to obtain well-calibrated probabilities from binary classifiers with beta calibration

Kull, M., Silva Filho, T. M., & Flach, P. (2017) · Electronic Journal of Statistics

Imbalanced Data Classification TechniquesMachine Learning and Data Classification

Cited by 111

Automating Mendelian randomization through machine learning to construct a putative causal map of the human phenome

Hemani, G., Bowden, J., Haycock, P., Zheng, J., Davis, O., Flach, P., Gaunt, T., & Smith, G. D. (2017) · bioRxiv (Cold Spring Harbor Laboratory)

Genetic Associations and EpidemiologyAdvanced Causal Inference Techniques

Cited by 85

Human Activity Recognition Based on Dynamic Active Learning

Bi, H., Perello-Nieto, M., Santos-Rodriguez, R., & Flach, P. (2021) · IEEE Journal of Biomedical and Health Informatics

Context-Aware Activity Recognition SystemsMachine Learning and Algorithms

Cited by 81

Machine learning to assist risk-of-bias assessments in systematic reviews

Millard, L. A., Flach, P. A., & Higgins, J. P. (2016) · International Journal of Epidemiology

Meta-analysis and systematic reviewsComputational and Text Analysis Methods

Cited by 65

Novel Decompositions of Proper Scoring Rules for Classification: Score Adjustment as Precursor to Calibration

Kull, M., & Flach, P. (2015) · Lecture Notes in Computer Science

Imbalanced Data Classification TechniquesMachine Learning and Data Classification

Cited by 43

Efficient and Robust Model Benchmarks with Item Response Theory and Adaptive Testing

Song, H., & Flach, P. (2021) · International Journal of Interactive Multimedia and Artificial Intelligence

Machine Learning and Data ClassificationPsychometric Methodologies and Testing

Cited by 8