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
Geoffrey HolmesUniversity of WaikatoVerified email at waikato.ac.nz
Jesse ReadÉcole PolytechniqueVerified email at polytechnique.edu
Jaakko HollménStockholm University, SwedenVerified email at dsv.su.se
Georg KremplInformation and Computing Sciences, Utrecht University, The NetherlandsVerified email at uu.nl
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
Toon CaldersUniversity of AntwerpVerified email at uantwerpen.be
Abdelhamid BouchachiaBournemouth University, United KingdomVerified email at bournemouth.ac.uk
Bogdan GabrysProf. of Data Science, University of Technology SydneyVerified email at uts.edu.au
HUI TANGUniversity of OsloVerified email at geo.uio.no
Jorn BakkerPhD Candidate, Eindhoven University of TechnologyVerified email at tue.nl
Faisal KamiranInformation Technology University of The PunjabVerified email at itu.edu.pk
Ferhat KayaPhDVerified email at helsinki.fi
Faysal BibiMuseum für Naturkunde, BerlinVerified email at mfn.berlin
Tomas KrilavičiusProfessor of Computer Science, Faculty of Informatics, Vytautas Magnus UniversityVerified email at vdu.lt

Indrė Žliobaitė

University of Helsinki

Verified email at helsinki.fi - Homepage

data science concept drift palaeobiology macroecology #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	2803	2014
Learning under concept drift: an overview I Zliobaite arXiv preprint arXiv:1010.4784, 2009	587	2009
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	397	2013
An overview of concept drift applications I Žliobaitė, M Pechenizkiy, J Gama Big data analysis: new algorithms for a new society, 91-114, 2016	374	2016
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	358	2014
Handling concept drift in process mining RPJC Bose, WMP van der Aalst, I Žliobaitė, M Pechenizkiy Advanced Information Systems Engineering: 23rd International Conference …, 2011	268	2011
Measuring discrimination in algorithmic decision making I Žliobaitė Data Mining and Knowledge Discovery 31 (4), 1060-1089, 2017	250	2017
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	215	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	173	2013
A survey on measuring indirect discrimination in machine learning I Zliobaite arXiv preprint arXiv:1511.00148, 2015	172	2015
Handling conditional discrimination I Žliobaite, F Kamiran, T Calders 2011 IEEE 11th international conference on data mining, 992-1001, 2011	156	2011
On the relation between accuracy and fairness in binary classification I Zliobaite arXiv preprint arXiv:1505.05723, 2015	137	2015
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	135	2013
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	134	2016
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	134	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	109	2013
Active learning with evolving streaming data I Žliobaitė, A Bifet, B Pfahringer, G Holmes Machine Learning and Knowledge Discovery in Databases: European Conference …, 2011	105	2011
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	100	2012
Change with delayed labeling: When is it detectable? I Žliobaite 2010 IEEE international conference on data mining workshops, 843-850, 2010	90	2010
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	85	2016

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