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Zhao, J., Jiao, L., Xia, S., Basto-Fernandes, V., Yevseyeva, I., Zhou, Y....Emmerichd, M. T. M. (2018). Multiobjective sparse ensemble learning by means of evolutionary algorithms. Decision Support Systems. 111, 86-100
Exportar Referência (IEEE)
J. Zhao et al.,  "Multiobjective sparse ensemble learning by means of evolutionary algorithms", in Decision Support Systems, vol. 111, pp. 86-100, 2018
Exportar BibTeX
	author = "Zhao, J. and Jiao, L. and Xia, S. and Basto-Fernandes, V. and Yevseyeva, I. and Zhou, Y. and Emmerichd, M. T. M.",
	title = "Multiobjective sparse ensemble learning by means of evolutionary algorithms",
	journal = "Decision Support Systems",
	year = "2018",
	volume = "111",
	number = "",
	doi = "10.1016/j.dss.2018.05.003",
	pages = "86-100",
	url = "https://www.sciencedirect.com/science/article/pii/S016792361830085X?via%3Dihub"
Exportar RIS
TI  - Multiobjective sparse ensemble learning by means of evolutionary algorithms
T2  - Decision Support Systems
VL  - 111
AU  - Zhao, J.
AU  - Jiao, L.
AU  - Xia, S.
AU  - Basto-Fernandes, V.
AU  - Yevseyeva, I.
AU  - Zhou, Y.
AU  - Emmerichd, M. T. M.
PY  - 2018
SP  - 86-100
SN  - 0167-9236
DO  - 10.1016/j.dss.2018.05.003
UR  - https://www.sciencedirect.com/science/article/pii/S016792361830085X?via%3Dihub
AB  - Ensemble learning can improve the performance of individual classifiers by combining their decisions. The sparseness of ensemble learning has attracted much attention in recent years. In this paper, a novel multiobjective sparse ensemble learning (MOSEL) model is proposed. Firstly, to describe the ensemble classifiers more precisely the detection error trade-off (DET) curve is taken into consideration. The sparsity ratio (sr) is treated as the third objective to be minimized, in addition to false positive rate (fpr) and false negative rate (fnr) minimization. The MOSEL turns out to be augmented DET (ADET) convex hull maximization problem. Secondly, several evolutionary multiobjective algorithms are exploited to find sparse ensemble classifiers with strong performance. The relationship between the sparsity and the performance of ensemble classifiers on the ADET space is explained. Thirdly, an adaptive MOSEL classifiers selection method is designed to select the most suitable ensemble classifiers for a given dataset. The proposed MOSEL method is applied to well-known MNIST datasets and a real-world remote sensing image change detection problem, and several datasets are used to test the performance of the method on this problem. Experimental results based on both MNIST datasets and remote sensing image change detection show that MOSEL performs significantly better than conventional ensemble learning methods.
ER  -