Pairwise meta-rules for better meta-learning-based algorithm ranking Q Sun, B Pfahringer Machine learning 93 (1), 141-161, 2013 | 106 | 2013 |
Bagging Ensemble Selection Q Sun, B Pfahringer AI'11: The 24th Australasian Joint Conference on Artificial Intelligence …, 2011 | 54 | 2011 |
Bagging Ensemble Selection for Regression Q Sun, B Pfahringer AI'12: The 25th Australasian Joint Conference on Artificial Intelligence, 2012 | 29 | 2012 |
Full model selection in the space of data mining operators Q Sun, B Pfahringer, M Mayo GECCO'12: Proceedings of the 14th international conference on Genetic and …, 2012 | 20 | 2012 |
Towards a Framework for Designing Full Model Selection and Optimization Systems Q Sun, B Pfahringer, M Mayo MCS'13: The 11th International Workshop on Multiple Classifier Systems, 259--270, 2013 | 19 | 2013 |
Balancing utility and fairness against privacy in medical data A Chester, YS Koh, J Wicker, Q Sun, J Lee 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 1226-1233, 2020 | 10 | 2020 |
Meta-Learning and the Full Model Selection Problem Q Sun The University of Waikato, 2014 | 9 | 2014 |
Detecting protected health information with an incremental learning ensemble: A case study on new zealand clinical text B Singh, Q Sun, YS Koh, J Lee, E Zhang 2020 ieee 7th international conference on data science and advanced …, 2020 | 4 | 2020 |
Sampling-based prediction of algorithm runtime Q Sun The University of Waikato, 2009 | 4 | 2009 |
Hierarchical meta-rules for scalable meta-learning Q Sun, B Pfahringer Pacific rim international conference on artificial intelligence, 383-395, 2014 | 2 | 2014 |
Evolving Artificial Datasets to Improve Interpretable Classifiers M Mayo, Q Sun Evolutionary Computation (CEC), 2014 IEEE Congress on, 2014 | 1 | 2014 |