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Semantic ScholarPeter A. Flach
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- affiliation: University of Bristol, Department of Computer Science, UK
- affiliation: Tilburg University, The Netherlands
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2010 – today
- 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. KHD@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]
- [r3]
- [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. Machine Learning 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? Machine Learning 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. Industrial 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 Intelligent Systems 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 Explorations 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]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 - [c78]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 - [c77]Yu Chen, Peter A. Flach:
SVR-based Modelling for the MoReBikeS Challenge: Analysis, Visualisation and Prediction. DC@PKDD/ECML 2015 - [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]
- 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. Industrial 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 Generation Comp. 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. Machine Learning 92(1): 1-3 (2013) - [j27]José Hernández-Orallo, Peter A. Flach, César Ferri:
ROC curves in cost space. Machine Learning 93(1): 71-91 (2013) - [c69]Simon Price, Peter A. Flach:
A Higher-order data flow model for heterogeneous Big Data. BigData 2013: 569-574 - 2012
- [j26]Daniel P. Berrar, Peter A. Flach:
Caveats and pitfalls of ROC analysis in clinical microarray research (and how to avoid them). Briefings in Bioinformatics 13(1): 83-97 (2012) - [j25]José Hernández-Orallo, Peter A. Flach, César Ferri:
A unified view of performance metrics: translating threshold choice into expected classification loss. Journal of Machine Learning Research 13: 2813-2869 (2012) - [j24]Stephen Muggleton, Luc De Raedt, David Poole, Ivan Bratko, Peter A. Flach, Katsumi Inoue, Ashwin Srinivasan:
ILP turns 20 - Biography and future challenges. Machine Learning 86(1): 3-23 (2012) - [e6]Peter A. Flach, Tijl De Bie, Nello Cristianini:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings, Part I. Lecture Notes in Computer Science 7523, Springer 2012, ISBN 978-3-642-33459-7 [contents] - [e5]Peter A. Flach, Tijl De Bie, Nello Cristianini:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings, Part II. Lecture Notes in Computer Science 7524, Springer 2012, ISBN 978-3-642-33485-6 [contents] - 2011
- [j23]Peter A. Flach:
The Machine Learning journal: 25 years young. Machine Learning 82(3): 273-274 (2011) - [c68]José Hernández-Orallo, Peter A. Flach, Cèsar Ferri Ramirez:
Brier Curves: a New Cost-Based Visualisation of Classifier Performance. ICML 2011: 585-592 - [c67]Peter A. Flach, José Hernández-Orallo, Cèsar Ferri Ramirez:
A Coherent Interpretation of AUC as a Measure of Aggregated Classification Performance. ICML 2011: 657-664 - [c66]William Klement, Peter A. Flach, Nathalie Japkowicz, Stan Matwin:
Smooth Receiver Operating Characteristics (smROC) Curves. ECML/PKDD (2) 2011: 193-208 - [i5]José Hernández-Orallo, Peter A. Flach, Cèsar Ferri Ramirez:
Technical Note: Towards ROC Curves in Cost Space. CoRR abs/1107.5930 (2011) - [i4]Song Liu, Peter A. Flach, Nello Cristianini:
Generic Multiplicative Methods for Implementing Machine Learning Algorithms on MapReduce. CoRR abs/1111.2111 (2011) - [i3]José Hernández-Orallo, Peter A. Flach, César Ferri:
Threshold Choice Methods: the Missing Link. CoRR abs/1112.2640 (2011) - 2010
- [j22]Peter A. Flach:
The Machine Learning journal: 250 issues and counting. Machine Learning 81(3): 227-228 (2010) - [c65]Sebastian Spiegler, Peter A. Flach:
Enhanced Word Decomposition by Calibrating the Decision Threshold of Probabilistic Models and Using a Model Ensemble. ACL 2010: 375-383 - [c64]Sebastian Spiegler, Andrew van der Spuy, Peter A. Flach:
Ukwabelana - An open-source morphological Zulu corpus. COLING 2010: 1020-1028 - [c63]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. eScience 2010: 182-189 - [c62]Tarek Abudawood, Peter A. Flach:
The Advantages of Seed Examples in First-Order Multi-class Subgroup Discovery. ECAI 2010: 1113-1114 - [c61]
- [c60]
- [c59]Simon Price, Peter A. Flach, Sebastian Spiegler:
SubSift: a novel application of the vector space model to support the academic research process. WAPA 2010: 20-27 - [c58]Tarek Abudawood, Peter A. Flach:
Exploiting the High Predictive Power of Multi-class Subgroups. ACML 2010: 177-192 - [r2]
- [r1]
2000 – 2009
- 2009
- [j21]Antonis C. Kakas, Peter A. Flach:
Abduction and Induction in Artificial Intelligence. J. Applied Logic 7(3): 251 (2009) - [j20]Peter A. Flach, Sebastian Spiegler, Bruno Golénia, Simon Price, John Guiver, Ralf Herbrich, Thore Graepel, Mohammed J. Zaki:
Novel tools to streamline the conference review process: experiences from SIGKDD'09. SIGKDD Explorations 11(2): 63-67 (2009) - [j19]Kseniya B. Shalonova, Bruno Golénia, Peter A. Flach:
Towards Learning Morphology for Under-Resourced Fusional and Agglutinating Languages. IEEE Trans. Audio, Speech & Language Processing 17(5): 956-965 (2009) - [c57]William Klement, Peter A. Flach, Nathalie Japkowicz, Stan Matwin:
Cost-Based Sampling of Individual Instances. Canadian Conference on AI 2009: 86-97 - [c56]Bruno Golénia, Sebastian Spiegler, Peter A. Flach:
UNGRADE: UNsupervised GRAph DEcomposition. CLEF (Working Notes) 2009 - [c55]Sebastian Spiegler, Bruno Golénia, Peter A. Flach:
Unsupervised Word Decomposition with the Promodes Algorithm. CLEF (1) 2009: 625-632 - [c54]Bruno Golénia, Sebastian Spiegler, Peter A. Flach:
Unsupervised Morpheme Discovery with Ungrade. CLEF (1) 2009: 633-640 - [c53]Sebastian Spiegler, Bruno Golénia, Peter A. Flach:
PROMODES: A Probabilistic Generative Model for Word Decomposition. CLEF (Working Notes) 2009 - [c52]Susanne Hoche, David Hardcastle, Peter A. Flach:
Using Time Dependent Link Reduction to Improve the Efficiency of Topic Prediction in Co-Authorship Graphs. CompleNet 2009: 173-184 - [c51]Tarek Abudawood, Peter A. Flach:
Evaluation Measures for Multi-class Subgroup Discovery. ECML/PKDD (1) 2009: 35-50 - [e4]John F. Elder IV, Françoise Fogelman-Soulié, Peter A. Flach, Mohammed Javeed Zaki:
Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28 - July 1, 2009. ACM 2009, ISBN 978-1-60558-495-9 [contents] - 2008
- [c50]Susanne Hoche, Peter A. Flach, David Hardcastle:
A Fast Method for Property Prediction in Graph-Structured Data from Positive and Unlabelled Examples. ECAI 2008: 162-166 - [c49]Simon Price, Peter A. Flach:
Querying and Merging Heterogeneous Data by Approximate Joins on Higher-Order Terms. ILP 2008: 226-243 - [c48]Sebastian Spiegler, Bruno Golénia, Ksenia Shalonova, Peter A. Flach, Roger C. F. Tucker:
Learning the morphology of Zulu with different degrees of supervision. SLT 2008: 9-12 - 2007
- [j18]Susanne Hoche, Andreas Nürnberger, Peter A. Flach:
Network analysis in natural sciences and engineering. AI Commun. 20(4): 229-230 (2007) - [c47]Shaomin Wu, Peter A. Flach, Cèsar Ferri Ramirez:
An Improved Model Selection Heuristic for AUC. ECML 2007: 478-489 - [c46]Peter A. Flach, Edson Takashi Matsubara:
A Simple Lexicographic Ranker and Probability Estimator. ECML 2007: 575-582 - [c45]Peter A. Flach:
Putting Things in Order: On the Fundamental Role of Ranking in Classification and Probability Estimation. ECML/PKDD 2007: 2-3 - [i2]Peter A. Flach, Edson Takashi Matsubara:
On classification, ranking, and probability estimation. Probabilistic, Logical and Relational Learning - A Further Synthesis 2007 - 2006
- [c44]
- [c43]Kerstin Eder, Peter A. Flach, Hsiou-Wen Hsueh:
Towards Automating Simulation-Based Design Verification Using ILP. ILP 2006: 154-168 - 2005
- [j17]Tom Fawcett, Peter A. Flach:
A Response to Webb and Ting's On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions. Machine Learning 58(1): 33-38 (2005) - [j16]Johannes Fürnkranz, Peter A. Flach:
ROC 'n' Rule Learning - Towards a Better Understanding of Covering Algorithms. Machine Learning 58(1): 39-77 (2005) - [c42]Elias Gyftodimos, Peter A. Flach:
Combining Bayesian Networks with Higher-Order Data Representations. IDA 2005: 145-156 - [c41]
- [c40]Ronaldo C. Prati, Peter A. Flach:
ROCCER: An Algorithm for Rule Learning Based on ROC Analysis. IJCAI 2005: 823-828 - [i1]Elias Gyftodimos, Peter A. Flach:
Combining Bayesian Networks with Higher-Order Data Representations. Probabilistic, Logical and Relational Learning 2005 - 2004
- [j15]Nada Lavrac, Branko Kavsek, Peter A. Flach, Ljupco Todorovski:
Subgroup Discovery with CN2-SD. Journal of Machine Learning Research 5: 153-188 (2004) - [j14]Nada Lavrac, Bojan Cestnik, Dragan Gamberger, Peter A. Flach:
Decision Support Through Subgroup Discovery: Three Case Studies and the Lessons Learned. Machine Learning 57(1-2): 115-143 (2004) - [j13]Thomas Gärtner, John W. Lloyd, Peter A. Flach:
Kernels and Distances for Structured Data. Machine Learning 57(3): 205-232 (2004) - [j12]Peter A. Flach, Nicolas Lachiche:
Naive Bayesian Classification of Structured Data. Machine Learning 57(3): 233-269 (2004) - [j11]José Hernández-Orallo, César Ferri, Nicolas Lachiche, Peter A. Flach:
The 1st workshop on ROC analysis in artificial intelligence (ROCAI-2004). SIGKDD Explorations 6(2): 159-161 (2004) - [j10]Peter A. Flach:
Book review: Logic for Learning: Learning Comprehensible Theories from Structured Data by John W. Lloyd, Springer-Verlag, 2003, ISBN 3-540-42027-4. TPLP 4(5-6): 753-755 (2004) - [c39]Johannes Fürnkranz, Peter A. Flach:
An Analysis of Stopping and Filtering Criteria for Rule Learning. ECML 2004: 123-133 - [c38]Annalisa Appice, Michelangelo Ceci, Simon Alan Rawles, Peter A. Flach:
Redundant feature elimination for multi-class problems. ICML 2004 - [c37]
- [c36]Elias Gyftodimos, Peter A. Flach:
Hierarchical Bayesian Networks: An Approach to Classification and Learning for Structured Data. SETN 2004: 291-300 - [e3]José Hernández-Orallo, César Ferri, Nicolas Lachiche, Peter A. Flach:
ROC Analysis in Artificial Intelligence, 1st International Workshop, ROCAI-2004, Valencia, Spain, August 22, 2004. 2004 [contents] - 2003
- [c35]Thomas Gärtner, Peter A. Flach, Stefan Wrobel:
On Graph Kernels: Hardness Results and Efficient Alternatives. COLT 2003: 129-143 - [c34]César Ferri, Peter A. Flach, José Hernández-Orallo:
Improving the AUC of Probabilistic Estimation Trees. ECML 2003: 121-132 - [c33]Peter A. Flach:
The Geometry of ROC Space: Understanding Machine Learning Metrics through ROC Isometrics. ICML 2003: 194-201 - [c32]
- [c31]Nicolas Lachiche, Peter A. Flach:
Improving Accuracy and Cost of Two-class and Multi-class Probabilistic Classifiers Using ROC Curves. ICML 2003: 416-423 - [c30]Mark-A. Krogel, Simon Alan Rawles, Filip Zelezný, Peter A. Flach, Nada Lavrac, Stefan Wrobel:
Comparative Evaluation of Approaches to Propositionalization. ILP 2003: 197-214 - [c29]
- 2002
- [j9]Tanja Urbancic, Maja Skrjanc, Peter A. Flach:
Web-based analysis of data mining and decision support education. AI Commun. 15(4): 199-204 (2002) - [c28]Peter A. Flach, Nada Lavrac:
Learning in Clausal Logic: A Perspective on Inductive Logic Programming. Computational Logic: Logic Programming and Beyond 2002: 437-471 - [c27]Yonghong Peng, Peter A. Flach, Carlos Soares, Pavel Brazdil:
Improved Dataset Characterisation for Meta-learning. Discovery Science 2002: 141-152 - [c26]Nada Lavrac, Peter A. Flach, Branko Kavsek, Ljupco Todorovski:
Adapting classification rule induction to subgroup discovery. ICDM 2002: 266-273 - [c25]César Ferri, Peter A. Flach, José Hernández-Orallo:
Learning Decision Trees Using the Area Under the ROC Curve. ICML 2002: 139-146 - [c24]Thomas Gärtner, Peter A. Flach, Adam Kowalczyk, Alexander J. Smola:
Multi-Instance Kernels. ICML 2002: 179-186 - [c23]
- [c22]
- [c21]Nada Lavrac, Filip Zelezný, Peter A. Flach:
RSD: Relational Subgroup Discovery through First-Order Feature Construction. ILP 2002: 149-165 - 2001
- [j8]Peter A. Flach:
On the state of the art in machine learning: A personal review. Artif. Intell. 131(1-2): 199-222 (2001) - [j7]Peter A. Flach, Nicolas Lachiche:
Confirmation-Guided Discovery of First-Order Rules with Tertius. Machine Learning 42(1/2): 61-95 (2001) - [j6]