| Metalearning: a survey of trends and technologies C Lemke, M Budka, B Gabrys Artificial intelligence review 44 (1), 117-130, 2015 | 271 | 2015 |
| Finding groups in data: Cluster analysis with ants U Boryczka, M Budka Applied Soft Computing 9 (1), 61-70, 2009 | 83 | 2009 |
| Link prediction based on subgraph evolution in dynamic social networks K Juszczyszyn, K Musial, M Budka 2011 IEEE Third International Conference on Privacy, Security, Risk and …, 2011 | 67 | 2011 |
| On accuracy of PDF divergence estimators and their applicability to representative data sampling M Budka, B Gabrys, K Musial Entropy 13 (7), 1229-1266, 2011 | 37 | 2011 |
| Towards cost-sensitive adaptation: When is it worth updating your predictive model? I Žliobaitė, M Budka, F Stahl Neurocomputing 150, 240-249, 2015 | 34 | 2015 |
| Laetoli’s lost tracks: 3D generated mean shape and missing footprints MR Bennett, SC Reynolds, SA Morse, M Budka Scientific reports 6 (1), 1-8, 2016 | 28 | 2016 |
| Density-preserving sampling: robust and efficient alternative to cross-validation for error estimation M Budka, B Gabrys IEEE transactions on neural networks and learning systems 24 (1), 22-34, 2012 | 27 | 2012 |
| Creation and growth of online social network K Musial, M Budka, K Juszczyszyn World Wide Web 16 (4), 421-447, 2013 | 23 | 2013 |
| Robust predictive modelling of water pollution using biomarker data M Budka, B Gabrys, E Ravagnan Water research 44 (10), 3294-3308, 2010 | 23 | 2010 |
| Stat-tracks and mediotypes: powerful tools for modern ichnology based on 3D models M Belvedere, MR Bennett, D Marty, M Budka, SC Reynolds, R Bakirov PeerJ 6, e4247, 2018 | 21 | 2018 |
| Towards automatic composition of multicomponent predictive systems MM Salvador, M Budka, B Gabrys International conference on hybrid artificial intelligence systems, 27-39, 2016 | 20 | 2016 |
| Footprints preserve terminal Pleistocene hunt? Human-sloth interactions in North America D Bustos, J Jakeway, TM Urban, VT Holliday, B Fenerty, DA Raichlen, ... Science advances 4 (4), eaar7621, 2018 | 19 | 2018 |
| Ridge regression ensemble for toxicity prediction M Budka, B Gabrys Procedia Computer Science 1 (1), 193-201, 2010 | 19 | 2010 |
| Cross-domain meta-learning for time-series forecasting AR Ali, B Gabrys, M Budka Procedia Computer Science 126, 9-18, 2018 | 17 | 2018 |
| The dynamic structural patterns of social networks based on triad transitions K Juszczyszyn, M Budka, K Musial 2011 International Conference on Advances in Social Networks Analysis and …, 2011 | 17 | 2011 |
| Automatic composition and optimization of multicomponent predictive systems with an extended auto-WEKA MM Salvador, M Budka, B Gabrys IEEE Transactions on Automation Science and Engineering 16 (2), 946-959, 2018 | 16* | 2018 |
| Footprints and human evolution: Homeostasis in foot function? MR Bennett, SC Reynolds, SA Morse, M Budka Palaeogeography, palaeoclimatology, palaeoecology 461, 214-223, 2016 | 16 | 2016 |
| Molecular model of dynamic social network based on e-mail communication M Budka, K Juszczyszyn, K Musial, A Musial Social Network Analysis and Mining 3 (3), 543-563, 2013 | 16 | 2013 |
| Predicting the evolution of social networks: optimal time window size for increased accuracy M Budka, K Musial, K Juszczyszyn 2012 International Conference on Privacy, Security, Risk and Trust and 2012 …, 2012 | 16 | 2012 |
| Correntropy-based density-preserving data sampling as an alternative to standard cross-validation M Budka, B Gabrys The 2010 International Joint Conference on Neural Networks (IJCNN), 1-8, 2010 | 15 | 2010 |
Bogdan GabrysProf. of Data Science, University of Technology SydneyVerified email at uts.edu.au
