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
Jaakko HollménStockholm University, SwedenVerified email at dsv.su.se
Jesse ReadÉcole PolytechniqueVerified email at polytechnique.edu
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
Bogdan GabrysProf. of Data Science, University of Technology SydneyVerified email at uts.edu.au
Abdelhamid BouchachiaBournemouth University, United KingdomVerified email at bournemouth.ac.uk
Jorn BakkerPhD Candidate, Eindhoven University of TechnologyVerified email at tue.nl
HUI TANGUniversity of OsloVerified email at geo.uio.no
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, 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	2534	2014
Learning under concept drift: an overview I Zliobaite arXiv preprint arXiv:1010.4784, 2009	561	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	368	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	348	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	333	2014
Handling concept drift in process mining RP Bose, WMP van der Aalst, I Žliobaitė, M Pechenizkiy International Conference on Advanced Information Systems Engineering, 391-405, 2011	252	2011
Measuring discrimination in algorithmic decision making I Žliobaitė Data Mining and Knowledge Discovery 31 (4), 1060-1089, 2017	222	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	193	2013
Why unbiased computational processes can lead to discriminative decision procedures T Calders, I Žliobaitė Discrimination and privacy in the information society, 43-57, 2013	157	2013
A survey on measuring indirect discrimination in machine learning I Zliobaite arXiv preprint arXiv:1511.00148, 2015	151	2015
Handling conditional discrimination I Žliobaite, F Kamiran, T Calders 2011 IEEE 11th International Conference on Data Mining, 992-1001, 2011	150	2011
On the relation between accuracy and fairness in binary classification I Zliobaite arXiv preprint arXiv:1505.05723, 2015	129	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 (2), 183-201, 2016	127	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 (3), 455-482, 2015	126	2015
Quantifying explainable discrimination and removing illegal discrimination in automated decision making F Kamiran, I Žliobaitė, T Calders Knowledge and information systems 35 (3), 613-644, 2013	120	2013
MOA concept drift active learning strategies for streaming data I Zliobaite, A Bifet, G Holmes, B Pfahringer Proceedings of the second workshop on applications of pattern analysis, 48-55, 2011	106	2011
Pitfalls in benchmarking data stream classification and how to avoid them A Bifet, J Read, I Žliobaitė, B Pfahringer, G Holmes Joint European conference on machine learning and knowledge discovery in …, 2013	103	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	99	2012
Change with delayed labeling: When is it detectable? I Žliobaite 2010 IEEE International Conference on Data Mining Workshops, 843-850, 2010	91	2010
On the window size for classification in changing environments LI Kuncheva, I Žliobaitė Intelligent Data Analysis 13 (6), 861-872, 2009	81	2009

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