Indrė Žliobaitė

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Mykola PechenizkiyEindhoven University of TechnologyVerified email at tue.nl
Albert BifetTélécom ParisTech & University of WaikatoVerified email at telecom-paristech.fr
Joao GamaProfessor, Faculty of Economics, University of Porto, and INESC TECVerified email at fep.up.pt
Mikael ForteliusEmeritus Professor of Evolutionary Palaeontology, University of HelsinkiVerified email at helsinki.fi
Bernhard PfahringerProfessor of Computer Science, University of WaikatoVerified email at waikato.ac.nz
Jesse ReadÉcole PolytechniqueVerified email at polytechnique.edu
Geoffrey HolmesUniversity of WaikatoVerified email at waikato.ac.nz
Jaakko HollménStockholm University, SwedenVerified email at dsv.su.se
Georg KremplInformation and Computing Sciences, Utrecht University, The NetherlandsVerified email at uu.nl
Toon CaldersUniversity of AntwerpVerified email at uantwerpen.be
Abdelhamid BouchachiaBournemouth University, United KingdomVerified email at bournemouth.ac.uk
HUI TANGFinnish Meteorological InstituteVerified email at fmi.fi
Bogdan GabrysProf. of Data Science, University of Technology SydneyVerified email at uts.edu.au
Juha SaarinenUniversity of HelsinkiVerified email at helsinki.fi
R. P. Jagadeesh Chandra BoseDepartment of Mathematics and Computer Science, Eindhoven University of Technology, The NetherlandsVerified email at tue.nl
Wil van der AalstProfessor of Process and Data Science, RWTH Aachen University || Celonis || Fraunhofer FIT || TU/eVerified email at pads.rwth-aachen.de
Ferhat KayaPhDVerified email at oulu.fi
Faysal BibiMuseum für Naturkunde, BerlinVerified email at mfn.berlin
Jerzy StefanowskiPoznan University of TechnologyVerified email at cs.put.poznan.pl
Jorn BakkerPhD Candidate, Eindhoven University of TechnologyVerified email at tue.nl

Indrė Žliobaitė

Professor, University of Helsinki

Verified email at helsinki.fi - Homepage

data science concept drift macroecology paleobiology #UnivHelsinkiCS


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A survey on concept drift adaptation J Gama, I Žliobaitė, A Bifet, M Pechenizkiy, A Bouchachia ACM computing surveys (CSUR) 46 (4), 1-37, 2014	3743	2014
Learning under concept drift: an overview I Zliobaite arXiv preprint arXiv:1010.4784, 2009	653	2009
An overview of concept drift applications I Žliobaitė, M Pechenizkiy, J Gama Big data analysis: new algorithms for a new society, 91-114, 2016	511	2016
Active learning with drifting streaming data I Žliobaitė, A Bifet, B Pfahringer, G Holmes IEEE transactions on neural networks and learning systems 25 (1), 27-39, 2013	452	2013
Open challenges for data stream mining research G Krempl, I Žliobaite, D Brzeziński, E Hüllermeier, M Last, V Lemaire, ... ACM SIGKDD explorations newsletter 16 (1), 1-10, 2014	411	2014
Measuring discrimination in algorithmic decision making I Žliobaitė Data Mining and Knowledge Discovery 31 (4), 1060-1089, 2017	357	2017
Handling concept drift in process mining RPJC Bose, WMP van der Aalst, I Žliobaitė, M Pechenizkiy Advanced Information Systems Engineering: 23rd International Conference …, 2011	308	2011
Dealing with concept drifts in process mining RPJC Bose, WMP Van Der Aalst, I Žliobaitė, M Pechenizkiy IEEE transactions on neural networks and learning systems 25 (1), 154-171, 2013	250	2013
Why unbiased computational processes can lead to discriminative decision procedures T Calders, I Žliobaitė Discrimination and Privacy in the Information Society: Data mining and …, 2013	241	2013
A survey on measuring indirect discrimination in machine learning I Zliobaite arXiv preprint arXiv:1511.00148, 2015	209	2015
Using sensitive personal data may be necessary for avoiding discrimination in data-driven decision models I Žliobaitė, B Custers Artificial Intelligence and Law 24, 183-201, 2016	200	2016
Handling conditional discrimination I Žliobaite, F Kamiran, T Calders 2011 IEEE 11th international conference on data mining, 992-1001, 2011	184	2011
Quantifying explainable discrimination and removing illegal discrimination in automated decision making F Kamiran, I Žliobaitė, T Calders Knowledge and information systems 35, 613-644, 2013	179	2013
On the relation between accuracy and fairness in binary classification I Zliobaite arXiv preprint arXiv:1505.05723, 2015	174	2015
Evaluation methods and decision theory for classification of streaming data with temporal dependence I Žliobaitė, A Bifet, J Read, B Pfahringer, G Holmes Machine Learning 98, 455-482, 2015	164	2015
Pitfalls in benchmarking data stream classification and how to avoid them A Bifet, J Read, I Žliobaitė, B Pfahringer, G Holmes Machine Learning and Knowledge Discovery in Databases: European Conference …, 2013	122	2013
Next challenges for adaptive learning systems I Zliobaite, A Bifet, M Gaber, B Gabrys, J Gama, L Minku, K Musial ACM SIGKDD Explorations Newsletter 14 (1), 48-55, 2012	121	2012
An ecometric analysis of the fossil mammal record of the Turkana Basin M Fortelius, I Žliobaitė, F Kaya, F Bibi, R Bobe, L Leakey, M Leakey, ... Philosophical Transactions of the Royal Society B: Biological Sciences 371 …, 2016	115	2016
Active learning with evolving streaming data I Žliobaitė, A Bifet, B Pfahringer, G Holmes Machine Learning and Knowledge Discovery in Databases: European Conference …, 2011	113	2011
The rise and fall of the Old World savannah fauna and the origins of the African savannah biome F Kaya, F Bibi, I Žliobaitė, JT Eronen, T Hui, M Fortelius Nature Ecology & Evolution 2 (2), 241-246, 2018	108	2018

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