Peter A. Flach

发表

Anomaly detection in star light curves using hierarchical Gaussian processes

Haoyan Chen, Tom Diethe, Peter A. Flach, 2018, ESANN.

Complex Networks: Results of the 1st International Workshop on Complex Networks (CompleNet 2009)

Peter A. Flach, Susanne Hoche, David Hardcastle, 2009 .

Multi-label Classification: A Comparative Study on Threshold Selection Methods

Peter A. Flach, Reem Al-Otaibi, Meelis Kull, 2014 .

Hybrid Multi-Label Decision Trees for Classification

Peter A. Flach, Reem Al-Otaibi, Meelis Kull, 2016 .

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

Peter A. Flach, Meelis Kull, Miquel Perelló-Nieto, 2019, NeurIPS.

Putting Things in Order: On the Fundamental Role of Ranking in Classification and Probability Estimation

Peter A. Flach, 2007, ECML/PKDD.

Abductive and inductive reasoning: background and issues

Peter A. Flach, Antonis C. Kakas, A. Kakas, 2000 .

Dataset Shift Detection with Model-Based Subgroup Discovery

Peter A. Flach, Hao Song, Georgios Kalogridis, 2015 .

Novel tools to streamline the conference review process: experiences from SIGKDD'09

Peter A. Flach, Mohammed J. Zaki, Thore Graepel, 2010, SKDD.

An Analysis of Rule Learning Heuristics

Johannes Fürnkranz, Peter A. Flach, 2003 .

Context modulation of Sensor Data Applied to Activity Recognition in Smart Homes

Niall Twomey, Peter A. Flach, 2014 .

Activity Recognition in Multiple Contexts for Smart-House Data

Peter A. Flach, Kacper Sokol, 2016, ILP.

First-Order Bayesian Classification with 1BC

Peter A. Flach, Nicolas Lachiche, N. Lachiche, 2007 .

ROCCER: An Algorithm for Rule Learning Based on ROC Analysis

Peter A. Flach, Ronaldo C. Prati, R. Prati, 2005, IJCAI.

SubSift: a novel application of the vector space model to support the academic research process

Peter A. Flach, Simon Price, Sebastian Spiegler, 2010, WAPA.

ADL™: A Topic Model for Discovery of Activities of Daily Living in a Smart Home

Tom Diethe, Peter A. Flach, Yu Chen, 2016, IJCAI.

Abductive and Inductive Reasoning: Report of the ECAI'96 Workshop

Peter A. Flach, Antonis C. Kakas, 1997, Log. J. IGPL.

Fast Unsupervised Online Drift Detection

Stan Matwin, Peter A. Flach, Gustavo E. A. P. A. Batista, 2016, Knowledge Discovery and Data Mining.

The 19th European Conference on Artificial Intelligence (ECAI'10)

Peter A. Flach, Tarek Abudawood, Tarek Abudawood, 2010 .

ROC Analysis in Artificial Intelligence, 1st International Workshop, ROCAI-2004, Valencia, Spain, August 22, 2004

Peter A. Flach, José Hernández-Orallo, Nicolas Lachiche, 2004, ROCAI.

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence Evolution of the contours of AI

Peter A. Flach, José Hernández-Orallo, Bao Sheng Loe, 2018 .

Delegating classifiers

Peter A. Flach, José Hernández-Orallo, César Ferri, 2004, ICML.

Learning Multi-class Theories in ILP

Peter A. Flach, Tarek Abudawood, Tarek Abudawood, 2010, ILP.

Neighbourhoods of examples for detecting logical redundancy

Peter A. Flach, Simon Rawles, 2005 .

Data mining for decision support: Supporting marketing decisions through subgroup discovery

Peter A. Flach, Nada Lavrač, Bojan Cestnik, 2003 .

Technical Note: Towards ROC Curves in Cost Space pdf

Peter A. Flach, José Hernández-Orallo, César Ferri, 2011, ArXiv.

The 20th International Conference on Inductive Logic Programming (ILP'10)

Peter A. Flach, Tarek Abudawood, Tarek Abudawood, 2010 .

The Geometry of ROC Space: Understanding Machine Learning Metrics through ROC Isometrics

Peter A. Flach, 2003, ICML.

Abduction and Induction in AI: Report of the IJCAI'97 Workshop

Peter A. Flach, Antonis C. Kakas, 1998 .

On the Road to Knowledge

Luís Torgo, Dunja Mladenic, Peter A. Flach, 2003 .

Abduction and Induction

Peter A. Flach, Antonis C. Kakas, A. Kakas, 2000 .

Improved Distances for Structured Data

Peter A. Flach, Dimitrios Mavroeidis, 2003, ILP.

Canadian Conference on Artificial Intelligence

Stan Matwin, Peter A. Flach, Nathalie Japkowicz, 2009 .

The Machine Learning journal: 250 issues and counting

Peter A. Flach, 2010, Machine Learning.

Threshold Choice Methods: the Missing Link pdf

Peter A. Flach, José Hernández-Orallo, César Ferri, 2011, ArXiv.

Scored AUC Metrics for Classifier Evaluation and Selection

Peter A. Flach, Shaomin Wu, 2005 .

16th International Conference on Inductive Logic Programming

Peter A. Flach, Kerstin Eder, Hsiou-Wen Hsueh, 2006 .

PROMODES: A Probabilistic Generative Model for Word Decomposition

Peter A. Flach, Sebastian Spiegler, Bruno Golénia, 2009, CLEF.

Smart-Homes for eHealth: Uncertainty Management and Calibration

Tom Diethe, Peter A. Flach, Tom Diethe, 2015, NIPS 2015.

An Analysis of Stopping and Filtering Criteria for Rule Learning

Johannes Fürnkranz, Peter A. Flach, 2004, ECML.

SVR-based Modelling for the MoReBikeS Challenge: Analysis, Visualisation and Prediction

Peter A. Flach, Yu Chen, 2015, DC@PKDD/ECML.

The Role of Textualisation and Argumentation in Understanding the Machine Learning Process

Peter A. Flach, Kacper Sokol, 2017, IJCAI.

Unsupervised learning of sensor topologies for improving activity recognition in smart environments

Niall Twomey, Tom Diethe, Peter A. Flach, 2017, Neurocomputing.

Separating the wheat from the chaff with machine learning

Peter A. Flach, 2008 .

Multilingual Information Access Evaluation, Lecture Notes in Computer Science

Peter A. Flach, Sebastian Spiegler, Bruno Golenia, 2010 .

Glass-Box: Explaining AI Decisions With Counterfactual Statements Through Conversation With a Voice-enabled Virtual Assistant

Peter A. Flach, Kacper Sokol, Kacper Sokol, 2018, IJCAI.

Active transfer learning for activity recognition

Niall Twomey, Tom Diethe, Peter A. Flach, 2016, ESANN.

Using Time Dependent Link Reduction to Improve the Efficiency of Topic Prediction in Co-Authorship Graphs

Peter A. Flach, Susanne Hoche, David Hardcastle, 2009, CompleNet.

Spoken Language Technology Workshop, 2008

Peter A. Flach, Spiegler Sebastian, Golenia Bruno, 2008 .

Machine learning and knowledge discovery in databases : European conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012 : proceedings

Peter A. Flach, Tijl De Bie, Nello Cristianini, 2012 .

The 5th European Starting AI Researcher Symposium (STAIRS'10)

Peter A. Flach, Tarek Abudawood, Tarek Abudawood, 2010 .

Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28 - July 1, 2009

Peter A. Flach, Mohammed J. Zaki, John Elder, 2009, KDD.

Using abduction for induction of normal logic programs

Peter A. Flach, Oliver Ray, Antonis C. Kakas, 2006 .

Predictive Performance of Weghted Relative Accuracy

Peter A. Flach, Nada Lavrac, Ljupco Todorovski, 2000, PKDD.

Feature Selection with Labelled and Unlabelled Data

Peter A. Flach, Shaomin Wu, Shaomin Wu, 2002 .

The Logic of Induction

Peter A. Flach, 1952 .

Learning Decision Trees Using the Area Under the ROC Curve

Peter A. Flach, José Hernández-Orallo, César Ferri, 2002, ICML.

Rule Evaluation Measures: A Unifying View

Peter A. Flach, Nada Lavrac, Blaz Zupan, 1999, ILP.

β3-IRT: A New Item Response Model and its Applications pdf

Tom Diethe, Peter A. Flach, Yu Chen, 2019, AISTATS.

Machine Learning: ECML 2001

Luc De Raedt, Peter A. Flach, L. D. Raedt, 2001, Lecture Notes in Computer Science.

The 1st workshop on ROC analysis in artificial intelligence (ROCAI-2004)

Peter A. Flach, José Hernández-Orallo, Nicolas Lachiche, 2004, SKDD.

An Analysis of Rule Evaluation Metrics

Johannes Fürnkranz, Peter A. Flach, Johannes Fürnkranz, 2003, ICML.

Abductive and Inductive Reasoning: Essays on their Relation and Integration

Peter A. Flach, Antonis C. Kakas, A. Kakas, 2000 .

Cost-sensitive boosting algorithms: Do we really need them?

Peter A. Flach, Narayanan Unny Edakunni, Gavin Brown, 2016, Machine Learning.

Feature Construction and Calibration for Clustering Daily Load Curves from Smart-Meter Data

Peter A. Flach, Nanlin Jin, Reem Al-Otaibi, 2016, IEEE Trans. Ind. Informatics.

The SPHERE Challenge: Activity Recognition with Multimodal Sensor Data pdf

Niall Twomey, Tom Diethe, Peter A. Flach, 2016, ArXiv.

Reframing in context: A systematic approach for model reuse in machine learning

Adolfo Martínez Usó, Peter A. Flach, José Hernández-Orallo, 2016, AI Commun..

4th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD2000)

Peter A. Flach, Todorovski Ljupco, A. ZighedDjamel, 2000 .

Activities of Daily Living Ontology for Ubiquitous Systems: Development and Evaluation

Peter A. Flach, Emma Tonkin, Przemyslaw Woznowski, 2018, Sensors.

Hierarchical Bayesian Networks: An Approach to Classification and Learning for Structured Data

Peter A. Flach, Elias Gyftodimos, E. Gyftodimos, 2004, SETN.

Modelling Patient Behaviour Using IoT Sensor Data: a Case Study to Evaluate Techniques for Modelling Domestic Behaviour in Recovery from Total Hip Replacement Surgery

Peter A. Flach, Hao Song, Emma L. Tonkin, 2020, J. Heal. Informatics Res..

The Facets of Artificial Intelligence: A Framework to Track the Evolution of AI

Peter A. Flach, José Hernández-Orallo, Bao Sheng Loe, 2018, IJCAI.

The Machine Learning journal: 25 years young

Peter A. Flach, 2011, Machine Learning.

Ukwabelana - An open-source morphological Zulu corpus

Peter A. Flach, Sebastian Spiegler, Andrew van der Spuy, 2010, COLING.

Brier Curves: a New Cost-Based Visualisation of Classifier Performance

Peter A. Flach, José Hernández-Orallo, César Ferri, 2011, ICML.

A Unified View of Performance Metrics: Translating Threshold Choice into Expected Classification Loss C` Esar Ferri

Peter A. Flach, José Hernández-Orallo, César Ferri, 2012 .

Kernels and Distances for Structured Data

Thomas Gärtner, Peter A. Flach, John W. Lloyd, 2004, Machine Learning.

Decision Support Through Subgroup Discovery: Three Case Studies and the Lessons Learned

Peter A. Flach, Nada Lavrac, Dragan Gamberger, 2004, Machine Learning.

Probabilistic Sensor Fusion for Ambient Assisted Living pdf

Niall Twomey, Tom Diethe, Peter A. Flach, 2017, ArXiv.

Rate-Constrained Ranking and the Rate-Weighted AUC

Peter A. Flach, Julian P. T. Higgins, Louise A. C. Millard, 2014, ECML/PKDD.

Bayesian Active Transfer Learning in Smart Homes

Niall Twomey, Tom Diethe, Peter A. Flach, 2015 .

BDL.NET: Bayesian dictionary learning in Infer.NET

Niall Twomey, Tom Diethe, Peter A. Flach, 2016, 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP).

ROC curves in cost space

Peter A. Flach, José Hernández-Orallo, César Ferri, 2013, Machine Learning.

A Sensor Platform for HEalthcare in a Residential Environment. Large-Scale Online Learning and Decision Making

Niall Twomey, Tom Diethe, Peter A. Flach, 2014 .

Computational support for academic peer review

Peter A. Flach, Simon Price, Simon Price, 2017, Commun. ACM.

UNGRADE: UNsupervised GRAph DEcomposition

Peter A. Flach, Sebastian Spiegler, Bruno Golénia, 2009, CLEF.

A first-order representation for knowledge discovery and Bayesian classification on relational data

Peter A. Flach, Nicolas Lachiche, N. Lachiche, 2007 .

Releasing eHealth Analytics into the Wild: Lessons Learnt from the SPHERE Project

Niall Twomey, Tom Diethe, Peter A. Flach, 2018, KDD.

Unsupervised Morpheme Discovery with Ungrade

Peter A. Flach, Sebastian Spiegler, Bruno Golénia, 2009, CLEF.

First-Order Logic

Peter A. Flach, 2018, Encyclopedia of Machine Learning.

The Use of Functional and Logic Languages in Machine Learning

Peter A. Flach, 2000, WFLP.

Simply logical - intelligent reasoning by example

Peter A. Flach, 1994, Wiley professional computing.

CASP-DM: Context Aware Standard Process for Data Mining pdf

Peter A. Flach, José Hernández-Orallo, Nicolas Lachiche, 2017, ArXiv.

On Graph Kernels: Hardness Results and Efficient Alternatives

Thomas Gärtner, Peter A. Flach, Stefan Wrobel, 2003, COLT.

ROCCER: A ROC convex hull rule learning algorithm

Peter A. Flach, Ronaldo C. Prati, 2004 .

Strongly Typed Inductive Concept Learning

Peter A. Flach, John W. Lloyd, Christophe G. Giraud-Carrier, 1998, ILP.

Predicate Invention in Inductive Data Engineering

Peter A. Flach, 1993, ECML.

Network analysis in natural sciences and engineering

Peter A. Flach, Andreas Nürnberger, Susanne Hoche, 2007, AI Commun..

Proceedings of the ECAI'98 Workshop on Abduction and Induction in AI

Peter A. Flach, Antonis C. Kakas, A. Kakas, 1998 .

Report of the First International Workshop on Learning over Multiple Contexts (LMCE 2014)

Peter A. Flach, Nicolas Lachiche, César Ferri, 2015, SKDD.

48th Annual Meeting of the Association for Computational Linguistics (ACL 2010)

Peter A. Flach, Sebastian Spiegler, Sebastian Spiegler, 2010 .

Decision tree-based characterization for meta-learning

Peter A. Flach, Carlos Soares, Pavel Brazdil, 2002 .

Estimating whether partial FOAF descriptions describe the same individual

Peter A. Flach, Simon Price, Simon Rawles, 2004 .

An Improved Model Selection Heuristic for AUC

Peter A. Flach, Shaomin Wu, César Ferri, 2007, ECML.

Web-based analysis of data mining and decision support education

Maja Skrjanc, Peter A. Flach, Tanja Urbancic, 2002, AI Commun..

Analysis of Patient Domestic Activity in Recovery From Hip or Knee RePlacement Surgery: Modelling Wrist-worn Wearable RSSI and Accelerometer Data in The Wild

Peter A. Flach, Hao Song, Miquel Perelló-Nieto, 2018, KHD@IJCAI.

Activity recognition using conditional random field

Peter A. Flach, Megha Agarwal, Megha Agarwal, 2015, iWOAR.

Decision Support for Data Mining

Peter A. Flach, José Hernández-Orallo, Jan Struyf, 2003 .

A Model of Inductive Reasoning

Peter A. Flach, 1992, Logic at Work.

1BC2: A True First-Order Bayesian Classifier

Peter A. Flach, Nicolas Lachiche, 2002, ILP.

Unsupervised Word Decomposition with the Promodes Algorithm

Peter A. Flach, Sebastian Spiegler, Bruno Golénia, 2009, CLEF.

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

Peter A. Flach, Daniel P. Berrar, D. Berrar, 2012, Briefings Bioinform..

Subgroup Discovery with Proper Scoring Rules

Peter A. Flach, Meelis Kull, Hao Song, 2016, ECML/PKDD.

ROC ‘n’ Rule Learning—Towards a Better Understanding of Covering Algorithms

Johannes Fürnkranz, Peter A. Flach, Johannes Fürnkranz, 2005, Machine Learning.

Precision-Recall-Gain Curves: PR Analysis Done Right

Peter A. Flach, Meelis Kull, Meelis Kull, 2015, NIPS.

Proceedings of the Ninth International Workshop on Inductive Logic Programming (ILP'99)

Peter A. Flach, Saso Dzeroski, S. Džeroski, 1999 .

Decomposing Probability Distributions on Structured Individuals

Peter A. Flach, Nicolas Lachiche, 2000, ILP Work-in-progress reports.

IBC: A First-Order Bayesian Classifier

Peter A. Flach, Nicolas Lachiche, N. Lachiche, 1999, ILP.

A Comprehensive Study of Activity Recognition Using Accelerometers

Niall Twomey, Tom Diethe, Peter A. Flach, 2018, Informatics.

Decompositions, dependencies, and Bayesian networks

Peter A. Flach, Blaz Zupan, Nada Lavrač, 1999 .

Data Mining and Decision Support: Aspects of Integration and Collaboration

Peter A. Flach, José Hernández-Orallo, Hendrik Blockeel, 2003 .

First-Order Logic

Peter A. Flach, 2018, Encyclopedia of Machine Learning.

Counterfactual Explanations of Machine Learning Predictions: Opportunities and Challenges for AI Safety

Peter A. Flach, Kacper Sokol, Kacper Sokol, 2019, SafeAI@AAAI.

Introduction to the special issue on Data Science in Europe

Peter A. Flach, Myra Spiliopoulou, Berthold Lausen, 2018, International Journal of Data Science and Analytics.

Advances in Neural Information Processing Systems 28

Peter A. Flach, Meelis Kull, Meelis Kull, 2015 .

Repairing Concavities in ROC Curves

Peter A. Flach, Shaomin Wu, Shaomin Wu, 2005, IJCAI.

Proceedings of the 3rd International Workshop on Evaluation Methods for Machine Learning

Peter A. Flach, William Klement, W. Klement, 2008, ICML 2008.

Peter A. Flach, Nada Lavrač, N. Lavrac, 2003 .

Chapter 7 DECISION SUPPORT FOR DATA MINING An introduction to ROC analysis and its applications

Peter A. Flach, Hendrik Blockeel, Jan Struyf, 2003 .

Multi-Instance Kernels

Thomas Gärtner, Alexander J. Smola, Peter A. Flach, 2002, ICML.

Sixth IEEE International Conference on e-Science (e-Science 2010), Brisbane, Australia

Peter A. Flach, Christopher P. Bailey, Simon Price, 2010 .

SubSift Web Services and Workflows for Profiling and Comparing Scientists and Their Published Works

Peter A. Flach, Christopher Bailey, Simon Price, 2010, 2010 IEEE Sixth International Conference on e-Science.

Learning in Clausal Logic: A Perspective on Inductive Logic Programming

Peter A. Flach, Nada Lavrac, N. Lavrač, 2002, Computational Logic: Logic Programming and Beyond.

WBCsvm: Weighted Bayesian Classification based on Support Vector Machines

Thomas Gärtner, Peter A. Flach, 2001, ICML.

A Fast Method for Property Prediction in Graph-Structured Data from Positive and Unlabelled Examples

Peter A. Flach, Susanne Hoche, David Hardcastle, 2008, ECAI.

Adapting classification rule induction to subgroup discovery

Peter A. Flach, Branko Kavsek, Nada Lavrac, 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

Improving the AUC of Probabilistic Estimation Trees

Peter A. Flach, José Hernández-Orallo, César Ferri, 2003, ECML.

A Coherent Interpretation of AUC as a Measure of Aggregated Classification Performance

Peter A. Flach, José Hernández-Orallo, César Ferri, 2011, ICML.

Conversational Explanations of Machine Learning Predictions Through Class-contrastive Counterfactual Statements

Peter A. Flach, Kacper Sokol, Kacper Sokol, 2018, IJCAI.

Inductive characterisation of database relations

Peter A. Flach, 1991 .

Learning the morphology of Zulu with different degrees of supervision

Peter A. Flach, Roger C. F. Tucker, Sebastian Spiegler, 2008, 2008 IEEE Spoken Language Technology Workshop.

FACE: Feasible and Actionable Counterfactual Explanations

Peter A. Flach, Tijl De Bie, Kacper Sokol, 2020, AIES.

LaCova: A Tree-Based Multi-label Classifier Using Label Covariance as Splitting Criterion

Peter A. Flach, Meelis Kull, Reem Al-Otaibi, 2014, 2014 13th International Conference on Machine Learning and Applications.

Proceedings ot the 2006 UK Workshop on Computational Intelligence (UKCI2006)

Peter A. Flach, Susanne Hoche, S. Hoche, 2006 .

On the relation between abduction and inductive learning

Peter A. Flach, Antonis C. Kakas, A. Kakas, 2000 .

Data Mining for Decision Support

Peter A. Flach, Dragan Gamberger, Nada Lavrač, 2003 .

Proceedings of the 12th European Conference on Machine Learning

Luc De Raedt, Peter A. Flach, L. D. Raedt, 2001 .

SubSift web services and workflows for profiling and comparing scientists and their published works

Peter A. Flach, Christopher Bailey, Simon Price, 2013, Future Gener. Comput. Syst..

Background Check: A General Technique to Build More Reliable and Versatile Classifiers

Peter A. Flach, Meelis Kull, Miquel Perelló-Nieto, 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

Cost-Based Sampling of Individual Instances

Stan Matwin, Peter A. Flach, Nathalie Japkowicz, 2009, Canadian Conference on AI.

A Machine Learning Approach to Objective Cardiac Event Detection

Niall Twomey, Peter A. Flach, N. Twomey, 2014, 2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems.

Comparative Evaluation of Approaches to Propositionalization

Peter A. Flach, Nada Lavrac, Stefan Wrobel, 2003, ILP.

Proceedings of the 28th International Conference on Machine Learning

Peter A. Flach, José Hernández-Orallo, Cèsar Ferri, 2011 .

Knowledge Representation for Inductive Learning

Peter A. Flach, 1999, ESCQARU.

Transactions and Change in Logic Databases

Peter A. Flach, 1998 .

18th European Conference on Machine Learning

Stan Matwin, Andrzej Skowron, Dunja Mladenic, 2007 .

Ninth International Workshop on Inductive Logic Programming (ILP'99)

Peter A. Flach, Saso Dzeroski, Blaz Zupan, 1999 .

Machine Learning and Knowledge Discovery in Databases

Peter A. Flach, Meelis Kull, N. Cristianini, 2012, Lecture Notes in Computer Science.

Encyclopedia of Machine Learning and Data Mining

Peter A. Flach, 2010 .

Towards a Theory of Inductive Logic Programming

Peter A. Flach, 1991, ISMIS.

A Relational Algebra for Basic Terms in a Higher-Order Logic

Peter A. Flach, Simon Price, Simon Price, 2013 .

Guest editors’ introduction: special issue of selected papers from ECML-PKDD 2012

Peter A. Flach, Tijl De Bie, 2013, Machine Learning.

5th International Conference on Deductive and Object-Oriented Databases (DOOD'97);

Peter A. Flach, 1997 .

Subgroup Discovery with CN2-SD

Peter A. Flach, Branko Kavsek, Nada Lavrac, 2004, J. Mach. Learn. Res..

Proceedings ECAI'96 workshop on Abductive and Inductive Reasoning

Peter A. Flach, Antonis C. Kakas, A. Kakas, 1996 .

An Analysis of Various Forms of "Jumping to Conclusions"

Peter A. Flach, 1992, AII.

Comparing Consequence Relations

Peter A. Flach, 1998, KR.

Proceedings of the CompulogNet area meeting on representation issues in reasoning and learning

Peter A. Flach, Nada Lavrač, N. Lavrac, 1997 .

Desiderata for Interpretability: Explaining Decision Tree Predictions with Counterfactuals

Peter A. Flach, Kacper Sokol, 2019, AAAI.

FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems

Peter A. Flach, Kacper Sokol, Alexander Hepburn, 2020, J. Open Source Softw..

Non-Parametric Calibration of Probabilistic Regression pdf

Peter A. Flach, Meelis Kull, Hao Song, 2018, ArXiv.

Evaluation Measures for Multi-class Subgroup Discovery

Peter A. Flach, Tarek Abudawood, Tarek Abudawood, 2009, ECML/PKDD.

Querying and Merging Heterogeneous Data by Approximate Joins on Higher-Order Terms

Peter A. Flach, Simon Price, Simon Price, 2008, ILP.

The logic of learning: a brief introduction to Inductive Logic Programming

Peter A. Flach, 1998 .

Editorial: Inductive Logic Programming is Coming of Age

Peter A. Flach, Saso Dzeroski, 2004, Machine Learning.

Subgroup Discovery in Smart Electricity Meter Data

Peter A. Flach, Nanlin Jin, Arno J. Knobbe, 2014, IEEE Transactions on Industrial Informatics.

Generic Multiplicative Methods for Implementing Machine Learning Algorithms on MapReduce pdf

Nello Cristianini, Peter A. Flach, Song Liu, 2011, ArXiv.

Naive Bayesian Classification of Structured Data

Peter A. Flach, Nicolas Lachiche, N. Lachiche, 2004, Machine Learning.

Bayesian Active Transfer Learning in Smart Homes, Advances in Active Learning: Bridging Theory and Practice

Niall Twomey, Tom Diethe, Peter A. Flach, 2015 .

Rule induction for subgroup discovery with CN2-SD

Peter A. Flach, Nada Lavrač, L Todorovski, 2002 .

A Big Data platform for smart meter data analytics

Peter A. Flach, Nanlin Jin, Joshua Thumim, 2019, Comput. Ind..

Model Reuse with Subgroup Discovery

Peter A. Flach, Hao Song, 2015, DC@PKDD/ECML.

Proceedings of the ECML/PKDD 2015 Discovery Challenges co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2015), Porto, Portugal, September 7-11, 2015

Peter A. Flach, Yu Chen, Yu Chen, 2015, DC@ECML/PKDD.

The 3rd York Doctoral Symposium on Computing (YDS'10)

Peter A. Flach, Tarek Abudawood, Tarek Abudawood, 2010 .

Predicting Topics of Scientific Papers from Co-Authorship Graphs: a Case Study

Peter A. Flach, Susanne Hoche, 2006 .

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

Tom Diethe, Peter A. Flach, Yu Chen, 2016, AAAI 2016.

Confirmation-Guided Discovery of First-Order Rules with Tertius

Peter A. Flach, Nicolas Lachiche, N. Lachiche, 2004, Machine Learning.

Enhanced Word Decomposition by Calibrating the Decision Threshold of Probabilistic Models and Using a Model Ensemble

Peter A. Flach, Sebastian Spiegler, 2010, ACL.

Improved Dataset Characterisation for Meta-learning

Peter A. Flach, Yonghong Peng, Carlos Soares, 2002, Discovery Science.

Classifier Calibration

Peter A. Flach, 2017, Encyclopedia of Machine Learning and Data Mining.

Peter A. Flach, 2010, Encyclopedia of Machine Learning and Data Mining.

Bayesian Modelling of the Temporal Aspects of Smart Home Activity with Circular Statistics

Niall Twomey, Tom Diethe, Peter A. Flach, 2015, ECML/PKDD.

Cooking up integrity constraints with PRIMUS (preliminary report)

Peter A. Flach, Nicolas Lachiche, 1997 .

First-Order Multi-class Subgroup Discovery

Peter A. Flach, Tarek Abudawood, Tarek Abudawood, 2010, STAIRS.

GLU: a software package for analysing continuously measured glucose levels in epidemiology

Peter A. Flach, Louise A C Millard, Kate Tilling, 2020, International journal of epidemiology.

On the need for structure modelling in sequence prediction

Niall Twomey, Tom Diethe, Peter A. Flach, 2016, Machine Learning.

Inductive Logic Databases: From Extensional to Intensional Knowledge

Peter A. Flach, 1997, DOOD.

A Response to Webb and Ting’s On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions

Peter A. Flach, Tom Fawcett, Tom Fawcett, 2005, Machine Learning.

Hierarchical Bayesian Networks: A Probabilistic Reasoning Model for Structured Domains

Peter A. Flach, Elias Gyftodimos, E. Gyftodimos, 2002 .

Machine Learning: EMCL 2001, 12th European Conference on Machine Learning, Freiburg, Germany, September 5-7, 2001, Proceedings

Luc De Raedt, Peter A. Flach, 2001, ECML.

Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers

Peter A. Flach, Meelis Kull, Telmo de Menezes e Silva Filho, 2017, AISTATS.

Rationality Postulates for Induction

Peter A. Flach, 1996, TARK.

Multilingual Information Access Evaluation

Peter A. Flach, Sebastian Spiegler, Bruno Golenia, 2010 .

Logic for Learning: Learning Comprehensible Theories from Structured Data by John W. Lloyd, Springer-Verlag, 2003, hard cover: ISBN 3-540-42027-4, x + 256 pages

Peter A. Flach, 2004, Theory and Practice of Logic Programming.

Peter A. Flach, 2010, Encyclopedia of Machine Learning and Data Mining.

Conference Report: Abduction and Induction in AI; Logic, Proofs and Algorithms; Logic in Natural Language; Logic for Concurrency and Synchronisation (LOCUS)

Peter A. Flach, Antonis C. Kakas, Ruy J. G. B. de Queiroz, 1998, Logic Journal of the IGPL.

FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparency pdf

Peter A. Flach, Raúl Santos-Rodríguez, Kacper Sokol, 2019, ArXiv.

Second-order Inductive Learning

Peter A. Flach, 1989, AII.

Towards Learning Morphology for Under-Resourced Fusional and Agglutinating Languages

Peter A. Flach, Bruno Golénia, Kseniya B. Shalonova, 2009, IEEE Transactions on Audio, Speech, and Language Processing.

A Higher-order data flow model for heterogeneous Big Data

Peter A. Flach, Simon Price, Simon Price, 2013, 2013 IEEE International Conference on Big Data.

Normal Forms for Inductive Logic Programming

Peter A. Flach, 1997, ILP.

Reframing in Frequent Pattern Mining

Peter A. Flach, Nicolas Lachiche, Meelis Kull, 2015, 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI).

Database Dependency Discovery: A Machine Learning Approach

Peter A. Flach, Iztok Savnik, Iztok Savnik, 1999, AI Commun..

Declaratively Capturing Local Label Correlations with Multi-Label Trees

Peter A. Flach, Reem Al-Otaibi, Meelis Kull, 2016, European Conference on Artificial Intelligence.

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

Niall Twomey, Majid Mirmehdi, Tom Diethe, 2015, IEEE Intelligent Systems.

An application of hierarchical Gaussian processes to the detection of anomalies in star light curves

Niall Twomey, Haoyan Chen, Tom Diethe, 2019, Neurocomputing.

Improving Accuracy and Cost of Two-class and Multi-class Probabilistic Classifiers Using ROC Curves

Peter A. Flach, Nicolas Lachiche, N. Lachiche, 2003, ICML.

Combining Bayesian Networks with Higher-Order Data Representations

Peter A. Flach, Elias Gyftodimos, 2005, Probabilistic, Logical and Relational Learning.

JMLR Workshop and Conference Proceedings Volume 13: 2nd Asian Conference on Machine Learning (ACML'10)

Peter A. Flach, Qiang Yang, Masashi Sugiyama, 2010 .

Consistent Term Mappings, Term Partitions and Inverse Resolution

Peter A. Flach, Shan-Hwei Nienhuys-Cheng, 1991, EWSL.

From Extensional to Intensional Knowledge: Inductive Logic Programming Techniques and Their Application to Deductive Databases

Peter A. Flach, 1996, Transactions and Change in Logic Databases.

HyperStream: a Workflow Engine for Streaming Data pdf

Niall Twomey, Tom Diethe, Peter A. Flach, 2019, ArXiv.

Multi-label classification by label clustering based on covariance

Peter A. Flach, Meelis Kull, Reem M Al-Otaibi, 2015 .

Uni- and multivariate probability density models for numeric subgroup discovery

Peter A. Flach, Siegfried Nijssen, Marvin Meeng, 2020, Intell. Data Anal..

Machine learning and knowledge discovery in databases: ECML-PKDD Proceedings, Part II

Peter A. Flach, Tijl De Bie, Nello Cristianini, 2012 .

The Advantages of Seed Examples in First-Order Multi-class Subgroup Discovery

Peter A. Flach, Tarek Abudawood, 2010, ECAI.

Discovery of multivalued dependencies from relations

Peter A. Flach, Iztok Savnik, Iztok Savnik, 2000, Intell. Data Anal..

Exploiting the High Predictive Power of Multi-class Subgroups

Peter A. Flach, Tarek Abudawood, 2010, ACML.

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

Peter A. Flach, Meelis Kull, Meelis Kull, 2015, ECML/PKDD.

Setting decision thresholds when operating conditions are uncertain

Peter A. Flach, José Hernández-Orallo, Cèsar Ferri, 2019, Data Mining and Knowledge Discovery.

Soft Discretization to Enhance the Continuous Decision Tree Induction

Peter A. Flach, Y Peng, Y. Peng, 2001 .

Bayesian Active Learning with Evidence-Based Instance Selection

Niall Twomey, Tom Diethe, Peter A. Flach, 2015 .

An extended transformation approach to inductive logic programming

Peter A. Flach, Nada Lavrac, N. Lavrač, 2001, ACM Trans. Comput. Log..

Luc De Raedt, Katsumi Inoue, Ashwin Srinivasan, 2011, Machine Learning.

Multi-relational Data Mining: a perspective

Peter A. Flach, 2001, EPIA.

Smooth Receiver Operating Characteristics (smROC) Curves

Stan Matwin, Peter A. Flach, Nathalie Japkowicz, 2011, ECML/PKDD.

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

Peter A. Flach, Meelis Kull, Telmo de Menezes e Silva Filho, 2017, AISTATS 2017.

RSD: Relational Subgroup Discovery through First-Order Feature Construction

Peter A. Flach, Nada Lavrac, Filip Zelezný, 2002, ILP.

Abduction and Induction in Artificial Intelligence

Peter A. Flach, Antonis C. Kakas, 2009, J. Appl. Log..

Working Notes for the CLEF 2009 Workshop, Corfu, Greece

Peter A. Flach, Sebastian Spiegler, Bruno Golenia, 2009 .

On the state of the art in machine learning: A personal review

Peter A. Flach, 2001, Artif. Intell..

The role of feature construction in inductive rule learning

Peter A. Flach, Lavrac Nada, Raedt Luc De, 2000 .

Redundant feature elimination for multi-class problems

Michelangelo Ceci, Peter A. Flach, Annalisa Appice, 2004, ICML.

Fast Unsupervised Online Drift Detection Using Incremental Kolmogorov-Smirnov Test

Stan Matwin, Peter A. Flach, Gustavo E. A. P. A. Batista, 2016, KDD.

Towards Automating Simulation-Based Design Verification Using ILP

Peter A. Flach, Kerstin Eder, Hsiou-Wen Hsueh, 2007, ILP.

Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II

Peter A. Flach, Tijl De Bie, Nello Cristianini, 2012 .

Inductive Logic Programming : 9th International Workshop, ILP-99, Bled, Slovenia, June 1999 : Proceedings

Peter A. Flach, Saso Dzeroski, 1999 .

Rate-Oriented Point-Wise Confidence Bounds for ROC Curves

Peter A. Flach, Meelis Kull, Louise A. C. Millard, 2014, ECML/PKDD.

Machine Learning by Peter Flach

Peter A. Flach, 2012 .

On classification, ranking, and probability estimation

Peter A. Flach, Edson Takashi Matsubara, 2007, Probabilistic, Logical and Relational Learning - A Further Synthesis.

Reliability Maps: A Tool to Enhance Probability Estimates and Improve Classification Accuracy

Peter A. Flach, Meelis Kull, 2014, ECML/PKDD.

Reinventing Machine Learning with ROC Analysis

Peter A. Flach, 2006, IBERAMIA-SBIA.

ICML Workshop on Challenges and Applications of Grammar Induction

Peter A. Flach, Ksenia Shalonova, Ksenia Shalonova, 2007 .

Reflections on reciprocity in research

Peter A. Flach, 2020, Machine Learning.

Model selection for dynamic processes

Peter A. Flach, Shaomin Wu, Shaomin Wu, 2002 .

First-Order Logic

Peter A. Flach, Peter A. Flach, 1989, Encyclopedia of Machine Learning.

Versatile Decision Trees for Learning Over Multiple Contexts

Peter A. Flach, Reem Al-Otaibi, Meelis Kull, 2015, ECML/PKDD.

Guest editors’ introduction: special section of selected papers from ECML-PKDD 2012

Peter A. Flach, Tijl De Bie, P. Flach, 2013, Data Mining and Knowledge Discovery.

SPHERE: A sensor platform for healthcare in a residential environment

Niall Twomey, Majid Mirmehdi, Tom Diethe, 2017 .

A Simple Lexicographic Ranker and Probability Estimator

Peter A. Flach, Edson Takashi Matsubara, E. Matsubara, 2007, ECML.

PKDD2000 workshop on Data Mining, Decision Support, Meta-learning and ILP : Forum for Practical Problem Presentation and Prospective Solutions

Peter A. Flach, Nicolas Lachiche, Alípio Mário Jorge, 2000 .

Multi-label thresholding for cost-sensitive classification

Peter A. Flach, Reem Alotaibi, Reem M. Alotaibi, 2020, Neurocomputing.

Abduction and induction: essays on their relation and integration

Peter A. Flach, A. Kakas, 2000 .

Bottom-up induction of functional dependencies from relations

Peter A. Flach, I. Savnik, Iztok Savnik, 1993 .

Propositionalization approaches to relational data mining

Peter A. Flach, N. Lavrac, S. Kramer, 2001 .

ADL™: A Topic Model for Recognition of Activities of Daily Living in a SmartHome

Peter A. Flach, Yu Chen, Tom Diethe, 2016 .

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

Peter A. Flach, Telmo de Menezes e Silva Filho, Meelis Kull, 2017 .

Decision Tree-Based Data Characterization for Meta-Learning

Peter A. Flach, Carlos Soares, P. Brazdil, 2002 .

Knowledge Acquisition by Abduction for Skills Monitoring: Application to Surgical Skills

Peter A. Flach, M. J. Rupérez, J. Hernández-Orallo, 2016 .

CASP-DM: Context Aware Standard Process for Data Mining

Peter A. Flach, Lidia Contreras Ochando, J. Hernández-Orallo, 2017, ArXiv.

Multi-dimensional ROC Analysis with Decision Trees *

Peter A. Flach, J. Hernández-Orallo, C. Ferri-Ramírez, 2002 .

Multi-dimensional ROC Analysis with Decision Trees * Technical Report 17-Jan-2002

Peter A. Flach, J. Hernández-Orallo, C. Ferri-Ramírez, 2002 .

Decision Trees for Ranking: Effect of new smoothing methods, new splitting criteria and simple pruning methods

Peter A. Flach, J. Hernández-Orallo, C. Ferri, 2003 .

Patterns of dataset shift

Peter A. Flach, Meelis Kull, 2014 .

Efficient probabilistic models for inference and learning Individual

Peter A. Flach, 2003 .

Probabilistic reasoning with terms

Peter A. Flach, Nicolas Lachiche, Elias Gyftodimos, 2003 .

Mining and Mapping the Research Landscape

Peter A. Flach, Simon Price, 2013 .

Morphology learning using tree of aligned suffix rules

Peter A. Flach, Ksenia Shalonova, Ksenia Shalonova, 2007 .

A report on experiments with weighted relative accuracy in CN2

Peter A. Flach, Nada Lavrač, Ljupčo Todorovski, 2000 .

Additional material for the Ukwabelana Zulu corpus

Peter A. Flach, Sebastian Spiegler, Andrew van der Spuy, 2010 .

An active semi-supervised deep learning model for human activity recognition

Peter A. Flach, I. Craddock, Haixia Bi, 2022, Journal of Ambient Intelligence and Humanized Computing.

Workshop on Friend of a Friend, Social Networking and the Semantic Web (FOAF2004), Galway, Ireland

Peter A. Flach, Simon Rawles, Simon Price, 2004 .

A Linear Kernel on Strongly Typed Terms

Thomas Gärtner, Peter A. Flach, Thomas Gärtner, 2001 .

The dialectics of artificial intelligence

Peter A. Flach, 1991 .

Bottom-up Induction of Functional Dependencies fromRelationsIztok

Peter A. Flach, Iztok Savnik, 1993 .

Modifying ROC Curves to Incorporate Predicted Probabilities

Peter A. Flach, José Hernández-Orallo, Cèsar Ferri, 2005 .

Price , S . , & Flach , P . A . ( 2013 ) . A Relational Algebra for Basic Terms in a

Peter A. Flach, Simon Price, 2018 .

Data Mining on Thrombin Dataset

Peter A. Flach, Shaomin Wu, 2002 .

Encyclopedia of Machine Learning and Data Mining

Peter A. Flach, Geoffrey I. Webb, C. Sammut, 2017, Encyclopedia of Machine Learning and Data Mining.

Alpha and beta components of diversity of freshwater nematodes at different spatial scales in subtropical coastal lakes

Peter A. Flach, A. S. Melo, C. P. Ozorio, 2012 .

Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence pdf

Peter A. Flach, Kacper Sokol, Peter Flach, 2021, ArXiv.

What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components

Peter A. Flach, Raúl Santos-Rodríguez, Kacper Sokol, 2020, Journal of Open Source Education.

When small disjuncts abound, try lazy learning

Peter A. Flach, Antal van den Bosch, van den L.J. Herik, 2007 .

Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence

Peter A. Flach, Kacper Sokol, 2021, ArXiv.

FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparency

Peter A. Flach, Raúl Santos-Rodríguez, Kacper Sokol, 2019, Softw. Impacts.

Interpretable Representations in Explainable AI: From Theory to Practice

Peter A. Flach, Kacper Sokol, 2020, 2008.07007.

Co-Designing Personal Health? Multidisciplinary Benefits and Challenges in Informing Diabetes Self-Care Technologies

Peter A. Flach, Paul Marshall, Katarzyna Stawarz, 2021, Proc. ACM Hum. Comput. Interact..

LIMESegment: Meaningful, Realistic Time Series Explanations

Peter A. Flach, Torty Sivill, 2022, AISTATS.

Simply Logical - Intelligent Reasoning by Example (Fully Interactive Online Edition)

Peter A. Flach, Kacper Sokol, 2022, ArXiv.

Human Activity Recognition Based on Dynamic Active Learning

Peter A. Flach, Raul Santos-Rodriguez, Haixia Bi, 2020, IEEE Journal of Biomedical and Health Informatics.

Abduction, induction, and the logic of scientific knowledge development

Peter A. Flach, Oliver Ray, Antonis C. Kakas, 2006 .

An Empirical Re-Examination of Weighted Voting for k-NN

Walter Daelemans, Peter A. Flach, Jakub Zavrel, 1997 .

Explainability fact sheets: a framework for systematic assessment of explainable approaches

Peter A. Flach, Kacper Sokol, Peter Flach, 2019, FAT*.

Model-Based Reinforcement Learning for Type 1 Diabetes Blood Glucose Control pdf

Peter A. Flach, The Open University, Cardiff University, 2020, AAI4H@ECAI.

Continuous Adaptation with Online Meta-Learning for Non-Stationary Target Regression Tasks

Peter A. Flach, Raúl Santos-Rodríguez, Taku Yamagata, 2022, Signals.

FACE: Feasible and Actionable Counterfactual Explanations

Peter A. Flach, T. D. Bie, Raúl Santos-Rodríguez, 2019, AIES.

A new strategy for applying grammatical inference to image classification problems

Peter A. Flach, H. Pistori, A. Calway, 2013, 2013 IEEE International Conference on Industrial Technology (ICIT).

One Explanation Does Not Fit All

Peter A. Flach, Kacper Sokol, Peter Flach, 2020, KI - Künstliche Intelligenz.

Co-designing opportunities for Human-Centred Machine Learning in supporting Type 1 diabetes decision-making

Peter A. Flach, Dmitri S. Katz, A. Ayobi, 2023, Int. J. Hum. Comput. Stud..

Toward Enhanced Clinical Decision Support for Patients Undergoing a Hip or Knee Replacement: Focus Group and Interview Study With Surgeons

Peter A. Flach, S. Grant, A. Masullo, 2023, JMIR perioperative medicine.

bLIMEy: Surrogate Prediction Explanations Beyond LIME pdf

Peter A. Flach, Kacper Sokol, Alexander Hepburn, 2019, NeurIPS 2019.

Smart homes, private homes? An empirical study of technology researchers’ perceptions of ethical issues in developing smart-home health technologies

Peter A. Flach, Rachael Gooberman-Hill, Peter Flach, 2017, BMC medical ethics.

A Weblog Recommender using Machine Learning and Semantic Web Technologies

Peter A. Flach, Peter Flach, Marc Crean, 2003 .

Connrmation-guided Discovery of Rst-order Rules with Tertius

Peter A. Flach, N. Lachiche, 2000 .

Peter A. Flach, S. Muggleton, D. Poole, 2011, Machine Learning.

Collecting Food and Drink Intake Data With Voice Input: Development, Usability, and Acceptability Study

Peter A. Flach, L. Millard, K. Tilling, 2023, JMIR mHealth and uHealth.

Classifier Calibration: How to assess and improve predicted class probabilities: a survey pdf

Peter A. Flach, Meelis Kull, Peter Flach, 2021, ArXiv.

Semi-Discriminative Representation Loss for Online Continual Learning

Peter A. Flach, Yu Chen, Tom Diethe, 2020, 2006.11234.

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

Peter A. Flach, Kacper Sokol, 2020, ArXiv.

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

Peter A. Flach, Meelis Kull, Raúl Santos-Rodríguez, 2021, Machine Learning.

Density, abundance and distribution of the guiana dolphin, (Sotalia guianensis van Benéden, 1864) in Sepetiba Bay, Southeast Brazil

Peter A. Flach, L. Flach, Adriano Garcia Chiarello, 2023, J. Cetacean Res. Manage..

Machine Learning

Peter A. Flach, Peter Flach, 2012 .

Discriminative Representation Loss (DRL): A More Efficient Approach Than Gradient Re-projection in continual learning.

Peter A. Flach, Tom Diethe, Yu Chen, 2020 .

Bypassing Gradients Re-Projection with Episodic Memories in Online Continual Learning pdf

Tom Diethe, Peter A. Flach, Yu Chen, 2020, ArXiv.

LIMEtree: Consistent and Faithful Surrogate Explanations of Multiple Classes

Peter A. Flach, Kacper Sokol, 2020, 2005.01427.

Computational Support for Academic Peer Review: A Perspective from Artificial Intelligence

Peter A. Flach, 2016 .

The Weak Supervision Landscape

Peter A. Flach, Jesús Cid-Sueiro, Raúl Santos-Rodríguez, 2022, 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops).

β3-IRT: A New Item Response Model and its Applications

Peter A. Flach, Masashi Sugiyama, Yu Chen, 2019, AISTATS.

Setting decision thresholds when operating conditions are uncertain

Peter A. Flach, J. Hernández-Orallo, C. Ferri, 2019, Data Mining and Knowledge Discovery.

Shapley Sets: Feature Attribution via Recursive Function Decomposition

Peter A. Flach, Torty Sivill, 2023, arXiv.org.

LIMEtree: Interactively Customisable Explanations Based on Local Surrogate Multi-output Regression Trees pdf

Peter A. Flach, Kacper Sokol, Peter Flach, 2020, ArXiv.

On the need for structure modelling in sequence prediction

Peter A. Flach, Tom Diethe, N. Twomey, 2016, Machine Learning.

Data Mining on the Sisyphus Dataset: Evaluation and Integration of Results

Peter A. Flach, Wu Shaomin, T Gartner, 2001 .

WBC SVM : Weighted Bayesian Classification based on Support Vector Machines

Peter A. Flach, HOMAS, G ., 2001 .

Feature Construction and Calibration for Clustering Daily Load Curves from Smart-Meter Data

Peter A. Flach, Nanlin Jin, Reem Al-Otaibi, 2016, IEEE Transactions on Industrial Informatics.

MIDI-Draw: Sketching to Control Melody Generation

Peter A. Flach, Raúl Santos-Rodríguez, Tashi Namgyal, 2023, ArXiv.

A Unified View of Performance Metrics: Translating Threshold Choice into Expected Classification Loss C` Esar Ferri

Peter A. Flach, J. Hernández-Orallo, C. Ferri, 2012 .

Empirical Evaluation of Predictive Models

Peter A. Flach, 2022, SIGIR Forum.

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

Peter A. Flach, Peter Flach, Hao Song, 2021, Int. J. Interact. Multim. Artif. Intell..

First-Order Logic

Peter A. Flach, 2017, Encyclopedia of Machine Learning and Data Mining.

Peter A. Flach, 2017, Encyclopedia of Machine Learning and Data Mining.