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 | 3395 | 2014 |
Handbook of educational data mining C Romero, S Ventura, M Pechenizkiy, RSJ Baker CRC press, 2010 | 675 | 2010 |
Predicting students drop out: A case study GW Dekker, M Pechenizkiy, JM Vleeshouwers Proceedings of the 2nd International Conference on Educational Data Mining …, 2009 | 649 | 2009 |
Building classifiers with independency constraints T Calders, F Kamiran, M Pechenizkiy 2009 IEEE international conference on data mining workshops, 13-18, 2009 | 641 | 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 | 460 | 2016 |
Discrimination aware decision tree learning F Kamiran, T Calders, M Pechenizkiy ICDM 2010: IEEE International Conference on Data Mining, 869-874, 2010 | 438 | 2010 |
Diversity in search strategies for ensemble feature selection A Tsymbal, M Pechenizkiy, P Cunningham Information fusion 6 (1), 83-98, 2005 | 420 | 2005 |
What's your current stress level? Detection of stress patterns from GSR sensor data J Bakker, M Pechenizkiy, N Sidorova 2011 IEEE 11th international conference on data mining workshops, 573-580, 2011 | 406 | 2011 |
Dynamic integration of classifiers for handling concept drift A Tsymbal, M Pechenizkiy, P Cunningham, S Puuronen Information fusion 9 (1), 56-68, 2008 | 306 | 2008 |
Stress detection from speech and galvanic skin response signals H Kurniawan, AV Maslov, M Pechenizkiy CBMS 2013: Proceedings of the 26th IEEE international symposium on computer …, 2013 | 303 | 2013 |
AH 12 years later: a comprehensive survey of adaptive hypermedia methods and techniques E Knutov, P De Bra, M Pechenizkiy New review of hypermedia and multimedia 15 (1), 5-38, 2009 | 302 | 2009 |
Handling concept drift in process mining RPJC Bose, WMP van der Aalst, I Žliobaitė, M Pechenizkiy Advanced Information Systems Engineering: 23rd International Conference …, 2011 | 292 | 2011 |
On formalizing fairness in prediction with machine learning P Gajane, M Pechenizkiy arXiv preprint arXiv:1710.03184, 2017 | 267 | 2017 |
Feedback loop and bias amplification in recommender systems M Mansoury, H Abdollahpouri, M Pechenizkiy, B Mobasher, R Burke CIKM 2020: Proceedings of the 29th ACM international conference on …, 2020 | 245 | 2020 |
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 | 240 | 2013 |
Graph-based n-gram language identification on short texts E Tromp, M Pechenizkiy Proc. 20th Machine Learning conference of Belgium and The Netherlands, 27-34, 2011 | 158 | 2011 |
Introduction to the special section on educational data mining T Calders, M Pechenizkiy Acm Sigkdd Explorations Newsletter 13 (2), 3-6, 2012 | 140 | 2012 |
More convnets in the 2020s: Scaling up kernels beyond 51x51 using sparsity S Liu, T Chen, X Chen, X Chen, Q Xiao, B Wu, T Karkkainen, ... ICLR 2023 preprint arXiv:2207.03620, 2023 | 133 | 2023 |
Class noise and supervised learning in medical domains: The effect of feature extraction M Pechenizkiy, A Tsymbal, S Puuronen, O Pechenizkiy CBMS 2006: 19th IEEE Symposium on Computer-Based Medical Systems, 708-713, 2006 | 133 | 2006 |
Process mining from educational data N Trcˇka, M Pechenizkiy, W van der Aalst Handbook of educational data mining, 123-142, 2011 | 132 | 2011 |