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Multiobjective sparse ensemble learning by means of evolutionary algorithms
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Orientador(es)
Resumo(s)
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.
Descrição
Palavras-chave
Ensemble learning Change detection Multiobjective optimization Classification Sparse representation
Contexto Educativo
Citação
Jiaqi Zhao, Licheng Jiao, Shixiong Xia, Vitor Basto Fernandes, Iryna Yevseyeva, Yong Zhou, Michael T.M. Emmerich, Multiobjective sparse ensemble learning by means of evolutionary algorithms, Decision Support Systems, Volume 111, 2018, Pages 86-100, ISSN 0167-9236, https://doi.org/10.1016/j.dss.2018.05.003.
Editora
Elsevier BV