Indrė Žliobaitė
Cited by
Cited by
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
Learning under concept drift: an overview
I Zliobaite
arXiv preprint arXiv:1010.4784, 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
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
An overview of concept drift applications
I Žliobaitė, M Pechenizkiy, J Gama
Big data analysis: new algorithms for a new society, 91-114, 2016
Handling concept drift in process mining
RPJC Bose, WMP van der Aalst, I Žliobaitė, M Pechenizkiy
International Conference on Advanced Information Systems Engineering, 391-405, 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
Measuring discrimination in algorithmic decision making
I Žliobaitė
Data Mining and Knowledge Discovery 31 (4), 1060-1089, 2017
A survey on measuring indirect discrimination in machine learning
I Zliobaite
arXiv preprint arXiv:1511.00148, 2015
Why unbiased computational processes can lead to discriminative decision procedures
T Calders, I Žliobaitė
Discrimination and privacy in the information society, 43-57, 2013
Handling conditional discrimination
I Žliobaite, F Kamiran, T Calders
2011 IEEE 11th International Conference on Data Mining, 992-1001, 2011
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
On the relation between accuracy and fairness in binary classification
I Zliobaite
arXiv preprint arXiv:1505.05723, 2015
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
Active learning with evolving streaming data
I Žliobaitė, A Bifet, B Pfahringer, G Holmes
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2011
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
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
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
Change with delayed labeling: When is it detectable?
I Žliobaite
2010 IEEE International Conference on Data Mining Workshops, 843-850, 2010
On the window size for classification in changing environments
LI Kuncheva, I Žliobaitė
Intelligent Data Analysis 13 (6), 861-872, 2009
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