| Data-driven soft sensors in the process industry P Kadlec, B Gabrys, S Strandt Computers & chemical engineering 33 (4), 795-814, 2009 | 2156 | 2009 |
| Classifier selection for majority voting D Ruta, B Gabrys Information fusion 6 (1), 63-81, 2005 | 854 | 2005 |
| Review of adaptation mechanisms for data-driven soft sensors P Kadlec, R Grbić, B Gabrys Computers & chemical engineering 35 (1), 1-24, 2011 | 612 | 2011 |
| An overview of classifier fusion methods D Ruta, B Gabrys Computing and Information systems 7 (1), 1-10, 2000 | 574 | 2000 |
| General fuzzy min-max neural network for clustering and classification B Gabrys, A Bargiela IEEE transactions on neural networks 11 (3), 769-783, 2000 | 539 | 2000 |
| Metalearning: a survey of trends and technologies C Lemke, M Budka, B Gabrys Artificial intelligence review 44 (1), 117-130, 2015 | 529 | 2015 |
| Foundations and modeling of dynamic networks using dynamic graph neural networks: A survey J Skarding, B Gabrys, K Musial iEEE Access 9, 79143-79168, 2021 | 443 | 2021 |
| Meta-learning for time series forecasting and forecast combination C Lemke, B Gabrys Neurocomputing 73 (10-12), 2006-2016, 2010 | 352 | 2010 |
| Nats-bench: Benchmarking nas algorithms for architecture topology and size X Dong, L Liu, K Musial, B Gabrys IEEE transactions on pattern analysis and machine intelligence 44 (7), 3634-3646, 2021 | 283 | 2021 |
| An overview of self-adaptive technologies within virtual reality training N Vaughan, B Gabrys, VN Dubey Computer Science Review 22, 65-87, 2016 | 220 | 2016 |
| The security challenges in the IoT enabled cyber-physical systems and opportunities for evolutionary computing & other computational intelligence H He, C Maple, T Watson, A Tiwari, J Mehnen, Y Jin, B Gabrys 2016 IEEE congress on evolutionary computation (CEC), 1015-1021, 2016 | 217 | 2016 |
| Local learning‐based adaptive soft sensor for catalyst activation prediction P Kadlec, B Gabrys AIChE Journal 57 (5), 1288-1301, 2011 | 193 | 2011 |
| Multi-level graph convolutional networks for cross-platform anchor link prediction H Chen, H Yin, X Sun, T Chen, B Gabrys, K Musial Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 186 | 2020 |
| Genetic algorithms in classifier fusion B Gabrys, D Ruta Applied soft computing 6 (4), 337-347, 2006 | 148 | 2006 |
| 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 | 140 | 2012 |
| Adaptive community detection incorporating topology and content in social networks M Qin, D Jin, D He, B Gabrys, K Musial Proceedings of the 2017 IEEE/ACM International Conference on Advances in …, 2017 | 134 | 2017 |
| A theoretical analysis of the limits of majority voting errors for multiple classifier systems D Ruta, B Gabrys Pattern Analysis and Applications 5 (4), 333-350, 2002 | 108 | 2002 |
| Analysis of the correlation between majority voting error and the diversity measures in multiple classifier systems D Ruta, B Gabrys ICSC-NAISO Academic Press, 2001 | 103 | 2001 |
| Adaptive preprocessing for streaming data I Zliobaite, B Gabrys IEEE transactions on knowledge and data Engineering 26 (2), 309-321, 2012 | 98 | 2012 |
| Neuro-fuzzy approach to processing inputs with missing values in pattern recognition problems B Gabrys International Journal of Approximate Reasoning 30 (3), 149-179, 2002 | 94 | 2002 |
Katarzyna MusialProfessor in Network Science, University of Technology Sydney, AustraliaVerified email at uts.edu.au
