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Indrė Žliobaitė
Indrė Žliobaitė
University of Helsinki
在 helsinki.fi 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
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
33582014
Learning under concept drift: an overview
I Zliobaite
arXiv preprint arXiv:1010.4784, 2009
6342009
An overview of concept drift applications
I Žliobaitė, M Pechenizkiy, J Gama
Big data analysis: new algorithms for a new society, 91-114, 2016
4572016
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
4282013
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
3912014
Measuring discrimination in algorithmic decision making
I Žliobaitė
Data Mining and Knowledge Discovery 31 (4), 1060-1089, 2017
3162017
Handling concept drift in process mining
RPJC Bose, WMP van der Aalst, I Žliobaitė, M Pechenizkiy
Advanced Information Systems Engineering: 23rd International Conference …, 2011
2902011
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
2402013
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
2132013
A survey on measuring indirect discrimination in machine learning
I Zliobaite
arXiv preprint arXiv:1511.00148, 2015
1982015
Handling conditional discrimination
I Žliobaite, F Kamiran, T Calders
2011 IEEE 11th international conference on data mining, 992-1001, 2011
1752011
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
1682016
On the relation between accuracy and fairness in binary classification
I Zliobaite
arXiv preprint arXiv:1505.05723, 2015
1632015
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
1602013
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
1512015
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
1142013
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
1122012
Active learning with evolving streaming data
I Žliobaitė, A Bifet, B Pfahringer, G Holmes
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2011
1112011
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
1012016
Change with delayed labeling: When is it detectable?
I Žliobaite
2010 IEEE international conference on data mining workshops, 843-850, 2010
982010
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