Leandro Minku
Cited by
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Ensemble learning for data stream analysis: A survey
B Krawczyk, LL Minku, J Gama, J Stefanowski, M Woźniak
Information Fusion 37, 132-156, 2017
The impact of diversity on online ensemble learning in the presence of concept drift
LL Minku, AP White, X Yao
IEEE Transactions on Knowledge and Data Engineering 22 (5), 730-742, 2010
DDD: A New Ensemble Approach For Dealing With Concept Drift
L Minku, X Yao
Knowledge and Data Engineering, IEEE Transactions on 24 (4), 619-633, 2012
Resampling-based ensemble methods for online class imbalance learning
S Wang, LL Minku, X Yao
IEEE Transactions on Knowledge and Data Engineering 27 (5), 1356-1368, 2015
A systematic study of online class imbalance learning with concept drift
S Wang, LL Minku, X Yao
IEEE transactions on neural networks and learning systems 29 (10), 4802-4821, 2018
Ensembles and locality: Insight on improving software effort estimation
LL Minku, X Yao
Information and Software Technology 55 (8), 1512-1528, 2013
Online Ensemble Learning of Data Streams with Gradually Evolved Classes
Y Sun, K Tang, LL Minku, S Wang, X Yao
IEEE Transactions on Knowledge and Data Engineering 28 (6), 1532-1545, 2016
A learning framework for online class imbalance learning
S Wang, LL Minku, X Yao
2013 IEEE Symposium on Computational Intelligence and Ensemble Learning …, 2013
Software effort estimation as a multiobjective learning problem
LL Minku, X Yao
ACM Transactions on Software Engineering and Methodology (TOSEM) 22 (4), 1-32, 2013
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
Concept drift detection for online class imbalance learning
S Wang, LL Minku, D Ghezzi, D Caltabiano, P Tino, X Yao
The 2013 International Joint Conference on Neural Networks (IJCNN), 1-10, 2013
An empirical evaluation of ensemble adjustment methods for analogy-based effort estimation
M Azzeh, AB Nassif, LL Minku
Journal of Systems and Software 103, 36-52, 2015
A Q-learning-based memetic algorithm for multi-objective dynamic software project scheduling
XN Shen, LL Minku, N Marturi, YN Guo, Y Han
Information Sciences 428, 1-29, 2018
The impact of parameter tuning on software effort estimation using learning machines
L Song, LL Minku, X Yao
Proceedings of the 9th international conference on predictive models in …, 2013
Sharing data and models in software engineering
T Menzies, E Kocaguneli, B Turhan, L Minku, F Peters
Morgan Kaufmann, 2014
Class imbalance evolution and verification latency in just-in-time software defect prediction
GG Cabral, LL Minku, E Shihab, S Mujahid
2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE …, 2019
Online class imbalance learning and its applications in fault detection
S Wang, LL Minku, X Yao
International Journal of Computational Intelligence and Applications 12 (04 …, 2013
Dealing with Multiple Classes in Online Class Imbalance Learning
S Wang, LL Minku, X Yao
Proc. 25th Int. Joint Conf. Artificial Intelligence, IJCAI/AAAI Press, 2118-2124, 2016
How to make best use of cross-company data in software effort estimation?
LL Minku, X Yao
Proceedings of the 36th International Conference on Software Engineering …, 2014
Metaheuristics “In the Large”
J Swan, S Adriaensen, AEI Brownlee, K Hammond, CG Johnson, A Kheiri, ...
European Journal of Operational Research 297 (2), 393-406, 2022
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