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ORCIDPeter A. Flach
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- affiliation: University of Bristol, Department of Computer Science, UK
- affiliation: Tilburg University, The Netherlands
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2020 – today
- 2023
[j60]Katarzyna Stawarz
, Dmitri S. Katz, Amid Ayobi, Paul Marshall, Taku Yamagata, Raúl Santos-Rodríguez, Peter A. Flach, Aisling Ann O'Kane:
Co-designing opportunities for Human-Centred Machine Learning in supporting Type 1 diabetes decision-making. Int. J. Hum. Comput. Stud. 173: 103003 (2023)- [j59]Haixia Bi, Miquel Perelló-Nieto, Raúl Santos-Rodríguez, Peter A. Flach, Ian Craddock:
An active semi-supervised deep learning model for human activity recognition. J. Ambient Intell. Humaniz. Comput. 14(10): 13049-13065 (2023) - [j58]Telmo de Menezes e Silva Filho, Hao Song, Miquel Perelló-Nieto, Raúl Santos-Rodríguez, Meelis Kull, Peter A. Flach:
Classifier calibration: a survey on how to assess and improve predicted class probabilities. Mach. Learn. 112(9): 3211-3260 (2023) - [c114]Taku Yamagata, Emma L. Tonkin, Benjamin Arana Sanchez, Ian Craddock, Miquel Perelló-Nieto, Raúl Santos-Rodríguez, Weisong Yang, Peter A. Flach:
When the Ground Truth is not True: Modelling Human Biases in Temporal Annotations. PerCom Workshops 2023: 527-533 - [p1]Peter A. Flach, Kacper Sokol, Jan Wielemaker:
Simply Logical - The First Three Decades. Prolog: The Next 50 Years 2023: 184-193 - [i34]Taku Yamagata, Emma L. Tonkin, Benjamin Arana Sanchez, Ian Craddock, Miquel Perelló-Nieto, Raúl Santos-Rodríguez, Weisong Yang, Peter A. Flach:
When the Ground Truth is not True: Modelling Human Biases in Temporal Annotations. CoRR abs/2302.02706 (2023) - [i33]Tashi Namgyal, Peter A. Flach, Raúl Santos-Rodríguez:
MIDI-Draw: Sketching to Control Melody Generation. CoRR abs/2305.11605 (2023) - [i32]Torty Sivill, Peter A. Flach:
Shapley Sets: Feature Attribution via Recursive Function Decomposition. CoRR abs/2307.01777 (2023) - 2022
- [j57]Peter A. Flach:
Empirical Evaluation of Predictive Models: A keynote at ECIR 2022. SIGIR Forum 56(1): 2:1-2:5 (2022) - [j56]Kacper Sokol, Raúl Santos-Rodríguez, Peter A. Flach:
FAT Forensics: A Python toolbox for algorithmic fairness, accountability and transparency. Softw. Impacts 14: 100406 (2022) - [c113]Torty Sivill, Peter A. Flach:
LIMESegment: Meaningful, Realistic Time Series Explanations. AISTATS 2022: 3418-3433 - [c112]Taku Yamagata, Raúl Santos-Rodríguez, Robert J. Piechocki, Peter A. Flach:
Understanding Reinforcement Learning Based Localisation as a Probabilistic Inference Algorithm. ICANN (2) 2022: 111-122 - [c111]Yu Chen, Peter A. Flach:
Self-Enhancer: A Self-supervised Framework for Low-Supervision, Drifted Data with Significant Missing Values. ICANN (4) 2022: 455-458 - [c110]Rafael Poyiadzi, Daniel Bacaicoa-Barber, Jesús Cid-Sueiro, Miquel Perelló-Nieto, Peter A. Flach, Raúl Santos-Rodríguez:
The Weak Supervision Landscape. PerCom Workshops 2022: 218-223 - [i31]Rafael Poyiadzi, Daniel Bacaicoa-Barber, Jesús Cid-Sueiro, Miquel Perelló-Nieto, Peter A. Flach, Raúl Santos-Rodríguez:
The Weak Supervision Landscape. CoRR abs/2203.16282 (2022) - [i30]Peter A. Flach, Kacper Sokol:
Simply Logical - Intelligent Reasoning by Example (Fully Interactive Online Edition). CoRR abs/2208.06823 (2022) - [i29]Kacper Sokol, Alexander Hepburn, Rafael Poyiadzi, Matthew Clifford, Raúl Santos-Rodríguez, Peter A. Flach:
FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems. CoRR abs/2209.03805 (2022) - [i28]Kacper Sokol, Alexander Hepburn, Raúl Santos-Rodríguez, Peter A. Flach:
What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components. CoRR abs/2209.03813 (2022) - 2021
- [j55]Hao Song, Peter A. Flach:
Efficient and Robust Model Benchmarks with Item Response Theory and Adaptive Testing. Int. J. Interact. Multim. Artif. Intell. 6(5): 110-118 (2021) - [j54]Reem Alotaibi, Peter A. Flach:
Multi-label thresholding for cost-sensitive classification. Neurocomputing 436: 232-247 (2021) - [j53]Amid Ayobi, Katarzyna Stawarz, Dmitri S. Katz, Paul Marshall, Taku Yamagata, Raúl Santos-Rodríguez, Peter A. Flach, Aisling Ann O'Kane:
Co-Designing Personal Health? Multidisciplinary Benefits and Challenges in Informing Diabetes Self-Care Technologies. Proc. ACM Hum. Comput. Interact. 5(CSCW2): 457:1-457:26 (2021) - [j52]Haixia Bi, Miquel Perelló-Nieto, Raúl Santos-Rodríguez, Peter A. Flach:
Human Activity Recognition Based on Dynamic Active Learning. IEEE J. Biomed. Health Informatics 25(4): 922-934 (2021) - [j51]Fernando Martínez-Plumed, Lidia Contreras Ochando, Cèsar Ferri, José Hernández-Orallo, Meelis Kull, Nicolas Lachiche, María José Ramírez-Quintana, Peter A. Flach:
CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories. IEEE Trans. Knowl. Data Eng. 33(8): 3048-3061 (2021) - [c109]Amid Ayobi, Katarzyna Stawarz, Dmitri S. Katz, Paul Marshall, Taku Yamagata, Raúl Santos-Rodríguez, Peter A. Flach, Aisling Ann O'Kane:
Machine Learning Explanations as Boundary Objects: How AI Researchers Explain and Non-Experts Perceive Machine Learning. IUI Workshops 2021 - [i27]Yu Chen, Song Liu, Tom Diethe, Peter A. Flach:
Continual Density Ratio Estimation in an Online Setting. CoRR abs/2103.05276 (2021) - [i26]Kacper Sokol, Peter A. Flach:
You Only Write Thrice: Creating Documents, Computational Notebooks and Presentations From a Single Source. CoRR abs/2107.06639 (2021) - [i25]Stefan Radic Webster, Peter A. Flach:
Risk Sensitive Model-Based Reinforcement Learning using Uncertainty Guided Planning. CoRR abs/2111.04972 (2021) - [i24]Telmo de Menezes e Silva Filho, Hao Song, Miquel Perelló-Nieto, Raúl Santos-Rodríguez, Meelis Kull, Peter A. Flach:
Classifier Calibration: How to assess and improve predicted class probabilities: a survey. CoRR abs/2112.10327 (2021) - [i23]Kacper Sokol, Peter A. Flach:
Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence. CoRR abs/2112.14466 (2021) - 2020
- [j50]Marvin Meeng, Harm de Vries, Peter A. Flach, Siegfried Nijssen, Arno J. Knobbe:
Uni- and multivariate probability density models for numeric subgroup discovery. Intell. Data Anal. 24(6): 1403-1439 (2020) - [j49]Michael Holmes, Miquel Perelló-Nieto, Hao Song, Emma Tonkin, Sabrina Grant, Peter A. Flach:
Modelling Patient Behaviour Using IoT Sensor Data: a Case Study to Evaluate Techniques for Modelling Domestic Behaviour in Recovery from Total Hip Replacement Surgery. J. Heal. Informatics Res. 4(3): 238-260 (2020) - [j48]Kacper Sokol, Alexander Hepburn, Rafael Poyiadzi, Matthew Clifford, Raúl Santos-Rodríguez, Peter A. Flach:
FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems. J. Open Source Softw. 5(49): 1904 (2020) - [j47]Kacper Sokol, Peter A. Flach:
One Explanation Does Not Fit All. Künstliche Intell. 34(2): 235-250 (2020) - [j46]Peter A. Flach:
Reflections on reciprocity in research. Mach. Learn. 109(7): 1281-1285 (2020) - [c108]Rafael Poyiadzi, Kacper Sokol, Raúl Santos-Rodríguez, Tijl De Bie, Peter A. Flach:
FACE: Feasible and Actionable Counterfactual Explanations. AIES 2020: 344-350 - [c107]Taku Yamagata, Aisling Ann O'Kane, Amid Ayobi, Dmitri S. Katz, Katarzyna Stawarz, Paul Marshall, Peter A. Flach, Raúl Santos-Rodríguez:
Model-Based Reinforcement Learning for Type 1 Diabetes Blood Glucose Control. AAI4H@ECAI 2020: 72-77 - [c106]Kacper Sokol, Peter A. Flach:
Explainability fact sheets: a framework for systematic assessment of explainable approaches. FAT* 2020: 56-67 - [c105]Haixia Bi, Raúl Santos-Rodríguez, Peter A. Flach:
Polsar Image Classification via Robust Low-Rank Feature Extraction and Markov Random Field. IGARSS 2020: 708-711 - [i22]Kacper Sokol, Peter A. Flach:
One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency. CoRR abs/2001.09734 (2020) - [i21]Kacper Sokol, Peter A. Flach:
LIMEtree: Interactively Customisable Explanations Based on Local Surrogate Multi-output Regression Trees. CoRR abs/2005.01427 (2020) - [i20]Yu Chen, Tom Diethe, Peter A. Flach:
Bypassing Gradients Re-Projection with Episodic Memories in Online Continual Learning. CoRR abs/2006.11234 (2020) - [i19]Kacper Sokol, Peter A. Flach:
Towards Faithful and Meaningful Interpretable Representations. CoRR abs/2008.07007 (2020) - [i18]Taku Yamagata, Aisling Ann O'Kane, Amid Ayobi, Dmitri S. Katz, Katarzyna Stawarz, Paul Marshall, Peter A. Flach, Raúl Santos-Rodríguez:
Model-Based Reinforcement Learning for Type 1Diabetes Blood Glucose Control. CoRR abs/2010.06266 (2020)
2010 – 2019
- 2019
- [j45]Tom Wilcox, Nanlin Jin, Peter A. Flach, Joshua Thumim:
A Big Data platform for smart meter data analytics. Comput. Ind. 105: 250-259 (2019) - [j44]Cèsar Ferri, José Hernández-Orallo, Peter A. Flach:
Setting decision thresholds when operating conditions are uncertain. Data Min. Knowl. Discov. 33(4): 805-847 (2019) - [j43]Niall Twomey, Haoyan Chen, Tom Diethe, Peter A. Flach:
An application of hierarchical Gaussian processes to the detection of anomalies in star light curves. Neurocomputing 342: 152-163 (2019) - [c104]Peter A. Flach:
Performance Evaluation in Machine Learning: The Good, the Bad, the Ugly, and the Way Forward. AAAI 2019: 9808-9814 - [c103]Kacper Sokol, Peter A. Flach:
Counterfactual Explanations of Machine Learning Predictions: Opportunities and Challenges for AI Safety. SafeAI@AAAI 2019 - [c102]Kacper Sokol, Peter A. Flach:
Desiderata for Interpretability: Explaining Decision Tree Predictions with Counterfactuals. AAAI 2019: 10035-10036 - [c101]Yu Chen, Telmo de Menezes e Silva Filho, Ricardo B. C. Prudêncio, Tom Diethe, Peter A. Flach:
$β^3$-IRT: A New Item Response Model and its Applications. AISTATS 2019: 1013-1021 - [c100]Hao Song, Tom Diethe, Meelis Kull, Peter A. Flach:
Distribution calibration for regression. ICML 2019: 5897-5906 - [c99]Meelis Kull, Miquel Perelló-Nieto, Markus Kängsepp, Telmo de Menezes e Silva Filho, Hao Song, Peter A. Flach:
Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration. NeurIPS 2019: 12295-12305 - [i17]Yu Chen, Telmo de Menezes e Silva Filho, Ricardo B. C. Prudêncio, Tom Diethe, Peter A. Flach:
β3-IRT: A New Item Response Model and its Applications. CoRR abs/1903.04016 (2019) - [i16]Hao Song, Tom Diethe, Meelis Kull, Peter A. Flach:
Distribution Calibration for Regression. CoRR abs/1905.06023 (2019) - [i15]Tom Diethe, Meelis Kull, Niall Twomey, Kacper Sokol, Hao Song, Miquel Perelló-Nieto, Emma Tonkin, Peter A. Flach:
HyperStream: a Workflow Engine for Streaming Data. CoRR abs/1908.02858 (2019) - [i14]Kacper Sokol, Raúl Santos-Rodríguez, Peter A. Flach:
FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparency. CoRR abs/1909.05167 (2019) - [i13]Rafael Poyiadzi, Kacper Sokol, Raúl Santos-Rodriguez, Tijl De Bie, Peter A. Flach:
FACE: Feasible and Actionable Counterfactual Explanations. CoRR abs/1909.09369 (2019) - [i12]Meelis Kull, Miquel Perelló-Nieto, Markus Kängsepp, Telmo de Menezes e Silva Filho, Hao Song, Peter A. Flach:
Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration. CoRR abs/1910.12656 (2019) - [i11]Kacper Sokol, Alexander Hepburn, Raúl Santos-Rodríguez, Peter A. Flach:
bLIMEy: Surrogate Prediction Explanations Beyond LIME. CoRR abs/1910.13016 (2019) - [i10]Kacper Sokol, Peter A. Flach:
Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches. CoRR abs/1912.05100 (2019) - 2018
- [j42]Peter A. Flach, Myra Spiliopoulou, Serge Allegrezza, Matthias Böhmer, Burkhard Hess, Berthold Lausen:
Introduction to the special issue on Data Science in Europe. Int. J. Data Sci. Anal. 6(3): 163-165 (2018) - [j41]Niall Twomey, Tom Diethe, Xenofon Fafoutis, Atis Elsts, Ryan McConville, Peter A. Flach, Ian Craddock:
A Comprehensive Study of Activity Recognition Using Accelerometers. Informatics 5(2): 27 (2018) - [j40]Przemyslaw Woznowski, Emma Tonkin, Peter A. Flach:
Activities of Daily Living Ontology for Ubiquitous Systems: Development and Evaluation. Sensors 18(7): 2361 (2018) - [c98]Haoyan Chen, Tom Diethe, Niall Twomey, Peter A. Flach:
Anomaly detection in star light curves using hierarchical Gaussian processes. ESANN 2018 - [c97]Mike Holmes, Hao Song, Emma Tonkin, Miquel Perelló-Nieto, Sabrina Grant, Peter A. Flach:
Analysis of Patient Domestic Activity in Recovery From Hip or Knee RePlacement Surgery: Modelling Wrist-worn Wearable RSSI and Accelerometer Data in The Wild. KDH@IJCAI 2018: 13-20 - [c96]Fernando Martínez-Plumed, Bao Sheng Loe, Peter A. Flach, Seán Ó hÉigeartaigh, Karina Vold, José Hernández-Orallo:
The Facets of Artificial Intelligence: A Framework to Track the Evolution of AI. IJCAI 2018: 5180-5187 - [c95]Kacper Sokol, Peter A. Flach:
Conversational Explanations of Machine Learning Predictions Through Class-contrastive Counterfactual Statements. IJCAI 2018: 5785-5786 - [c94]Kacper Sokol, Peter A. Flach:
Glass-Box: Explaining AI Decisions With Counterfactual Statements Through Conversation With a Voice-enabled Virtual Assistant. IJCAI 2018: 5868-5870 - [c93]Tom Diethe, Mike Holmes, Meelis Kull, Miquel Perelló-Nieto, Kacper Sokol, Hao Song, Emma Tonkin, Niall Twomey, Peter A. Flach:
Releasing eHealth Analytics into the Wild: Lessons Learnt from the SPHERE Project. KDD 2018: 243-252 - [i9]Hao Song, Meelis Kull, Peter A. Flach:
Non-Parametric Calibration of Probabilistic Regression. CoRR abs/1806.07690 (2018) - 2017
- [j39]Simon Price, Peter A. Flach:
Computational support for academic peer review: a perspective from artificial intelligence. Commun. ACM 60(3): 70-79 (2017) - [j38]Niall Twomey, Tom Diethe, Ian Craddock, Peter A. Flach:
Unsupervised learning of sensor topologies for improving activity recognition in smart environments. Neurocomputing 234: 93-106 (2017) - [c92]Meelis Kull, Telmo de Menezes e Silva Filho, Peter A. Flach:
Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers. AISTATS 2017: 623-631 - [c91]Kacper Sokol, Peter A. Flach:
The Role of Textualisation and Argumentation in Understanding the Machine Learning Process. IJCAI 2017: 5211-5212 - [r5]Peter A. Flach:
Classifier Calibration. Encyclopedia of Machine Learning and Data Mining 2017: 210-217 - [r4]Peter A. Flach:
First-Order Logic. Encyclopedia of Machine Learning and Data Mining 2017: 515-521 - [r3]Peter A. Flach:
ROC Analysis. Encyclopedia of Machine Learning and Data Mining 2017: 1109-1116 - [i8]Tom Diethe, Niall Twomey, Meelis Kull, Peter A. Flach, Ian Craddock:
Probabilistic Sensor Fusion for Ambient Assisted Living. CoRR abs/1702.01209 (2017) - [i7]Fernando Martínez-Plumed, Lidia Contreras Ochando, César Ferri, Peter A. Flach, José Hernández-Orallo, Meelis Kull, Nicolas Lachiche, María José Ramírez-Quintana:
CASP-DM: Context Aware Standard Process for Data Mining. CoRR abs/1709.09003 (2017) - 2016
- [j37]José Hernández-Orallo, Adolfo Martínez Usó, Ricardo B. C. Prudêncio, Meelis Kull, Peter A. Flach, Chowdhury Farhan Ahmed, Nicolas Lachiche:
Reframing in context: A systematic approach for model reuse in machine learning. AI Commun. 29(5): 551-566 (2016) - [j36]Niall Twomey, Tom Diethe, Peter A. Flach:
On the need for structure modelling in sequence prediction. Mach. Learn. 104(2-3): 291-314 (2016) - [j35]Nikolaos Nikolaou, Narayanan Unny Edakunni, Meelis Kull, Peter A. Flach, Gavin Brown:
Cost-sensitive boosting algorithms: Do we really need them? Mach. Learn. 104(2-3): 359-384 (2016) - [j34]Reem Al-Otaibi, Nanlin Jin, Tom Wilcox, Peter A. Flach:
Feature Construction and Calibration for Clustering Daily Load Curves from Smart-Meter Data. IEEE Trans. Ind. Informatics 12(2): 645-654 (2016) - [c90]Reem Al-Otaibi, Meelis Kull, Peter A. Flach:
Declaratively Capturing Local Label Correlations with Multi-Label Trees. ECAI 2016: 1467-1475 - [c89]Tom Diethe, Niall Twomey, Peter A. Flach:
Active transfer learning for activity recognition. ESANN 2016 - [c88]Miquel Perelló-Nieto, Telmo de Menezes e Silva Filho, Meelis Kull, Peter A. Flach:
Background Check: A General Technique to Build More Reliable and Versatile Classifiers. ICDM 2016: 1143-1148 - [c87]Yu Chen, Tom Diethe, Peter A. Flach:
ADL™: A Topic Model for Discovery of Activities of Daily Living in a Smart Home. IJCAI 2016: 1404-1410 - [c86]Kacper Sokol, Peter A. Flach:
Activity Recognition in Multiple Contexts for Smart-House Data. ILP (Short Papers) 2016: 66-72 - [c85]Denis Moreira dos Reis, Peter A. Flach, Stan Matwin, Gustavo E. A. P. A. Batista:
Fast Unsupervised Online Drift Detection Using Incremental Kolmogorov-Smirnov Test. KDD 2016: 1545-1554 - [c84]Tom Diethe, Niall Twomey, Peter A. Flach:
BDL.NET: Bayesian dictionary learning in Infer.NET. MLSP 2016: 1-6 - [c83]Hao Song, Meelis Kull, Peter A. Flach, Georgios Kalogridis:
Subgroup Discovery with Proper Scoring Rules. ECML/PKDD (2) 2016: 492-510 - [i6]Niall Twomey, Tom Diethe, Meelis Kull, Hao Song, Massimo Camplani, Sion L. Hannuna, Xenofon Fafoutis, Ni Zhu, Pete Woznowski, Peter A. Flach, Ian Craddock:
The SPHERE Challenge: Activity Recognition with Multimodal Sensor Data. CoRR abs/1603.00797 (2016) - 2015
- [j33]Ni Zhu, Tom Diethe, Massimo Camplani, Lili Tao, Alison Burrows, Niall Twomey, Dritan Kaleshi, Majid Mirmehdi, Peter A. Flach, Ian Craddock:
Bridging e-Health and the Internet of Things: The SPHERE Project. IEEE Intell. Syst. 30(4): 39-46 (2015) - [j32]Cèsar Ferri Ramirez, Peter A. Flach, Nicolas Lachiche:
Report of the First International Workshop on Learning over Multiple Contexts (LMCE 2014). SIGKDD Explor. 17(1): 48-50 (2015) - [c82]Chowdhury Farhan Ahmed, Md. Samiullah, Nicolas Lachiche, Meelis Kull, Peter A. Flach:
Reframing in Frequent Pattern Mining. ICTAI 2015: 799-806 - [c81]Megha Agarwal, Peter A. Flach:
Activity recognition using conditional random field. iWOAR 2015: 4:1-4:8 - [c80]Peter A. Flach, Meelis Kull:
Precision-Recall-Gain Curves: PR Analysis Done Right. NIPS 2015: 838-846 - [c79]Reem Al-Otaibi, Ricardo B. C. Prudêncio, Meelis Kull, Peter A. Flach:
Versatile Decision Trees for Learning Over Multiple Contexts. ECML/PKDD (1) 2015: 184-199 - [c78]Yu Chen, Peter A. Flach:
SVR-based Modelling for the MoReBikeS Challenge: Analysis, Visualisation and Prediction. DC@PKDD/ECML 2015 - [c77]Meelis Kull, Peter A. Flach:
Novel Decompositions of Proper Scoring Rules for Classification: Score Adjustment as Precursor to Calibration. ECML/PKDD (1) 2015: 68-85 - [c76]Tom Diethe, Niall Twomey, Peter A. Flach:
Bayesian Modelling of the Temporal Aspects of Smart Home Activity with Circular Statistics. ECML/PKDD (2) 2015: 279-294 - [c75]Hao Song, Peter A. Flach:
Model Reuse with Subgroup Discovery. DC@PKDD/ECML 2015 - 2014
- [j31]Nanlin Jin, Peter A. Flach, Tom Wilcox, Royston Sellman, Joshua Thumim, Arno J. Knobbe:
Subgroup Discovery in Smart Electricity Meter Data. IEEE Trans. Ind. Informatics 10(2): 1327-1336 (2014) - [c74]Niall Twomey, Peter A. Flach:
A Machine Learning Approach to Objective Cardiac Event Detection. CISIS 2014: 519-524 - [c73]Reem Al-Otaibi, Meelis Kull, Peter A. Flach:
LaCova: A Tree-Based Multi-label Classifier Using Label Covariance as Splitting Criterion. ICMLA 2014: 74-79 - [c72]Meelis Kull, Peter A. Flach:
Reliability Maps: A Tool to Enhance Probability Estimates and Improve Classification Accuracy. ECML/PKDD (2) 2014: 18-33 - [c71]Louise A. C. Millard, Peter A. Flach, Julian P. T. Higgins:
Rate-Constrained Ranking and the Rate-Weighted AUC. ECML/PKDD (2) 2014: 386-403 - [c70]Louise A. C. Millard, Meelis Kull, Peter A. Flach:
Rate-Oriented Point-Wise Confidence Bounds for ROC Curves. ECML/PKDD (2) 2014: 404-421 - 2013
- [j30]Tijl De Bie, Peter A. Flach:
Guest editors' introduction: special section of selected papers from ECML-PKDD 2012. Data Min. Knowl. Discov. 27(3): 442-443 (2013) - [j29]Simon Price, Peter A. Flach, Sebastian Spiegler, Christopher Bailey, Nikki Rogers:
SubSift web services and workflows for profiling and comparing scientists and their published works. Future Gener. Comput. Syst. 29(2): 569-581 (2013) - [j28]Tijl De Bie, Peter A. Flach:
Guest editors' introduction: special issue of selected papers from ECML-PKDD 2012. Mach. Learn. 92(1): 1-3 (2013) - [j27]José Hernández-Orallo, Peter A. Flach, César Ferri:
ROC curves in cost space. Mach. Learn. 93(1): 71-91 (2013) - [c69]Simon Price, Peter A. Flach:
A Higher-order data flow model for heterogeneous Big Data. IEEE BigData 2013: 569-574 - 2012
- [b2]Peter A. Flach:
Machine Learning - The Art and Science of Algorithms that Make Sense of Data. Cambridge University Press 2012, ISBN 978-1-10-742222-3, pp. I-XVII, 1-396 - [j26]Daniel P. Berrar, Peter A. Flach:
Caveats and pitfalls of ROC analysis in clinical microarray research (and how to avoid them). Briefings Bioinform. 13(1): 83-97 (2012) - [j25]