Multiobjective sparse ensemble learning by means of evolutionary algorithms

• Published in: Decision Support Systems Vol. 111; pp. 86 - 100
• Main Authors: Zhao, Jiaqi, Jiao, Licheng, Xia, Shixiong, Basto Fernandes, Vitor, Yevseyeva, Iryna, Zhou, Yong, T.M. Emmerich, Michael
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.07.2018; Elsevier Sequoia S.A
• Subjects: Algorithms; Change detection; Classification; Classifiers; Datasets; Ensemble learning; Error detection; Evolutionary algorithms; Image detection; Machine learning; Multiobjective optimization; Multiple objective analysis; Optimization; Performance enhancement; Remote sensing; Sparse representation; Sparsity; Studies; Teaching methods; Sparse representation; Ensemble learning; Change detection; Multiobjective optimization; Classification
• ISSN: 0167-9236; 1873-5797
• DOI: 10.1016/j.dss.2018.05.003

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. •A novel multiobjective sparse ensemble learning (MOSEL) model is proposed.•The relationship between the sparsity and the performance of ensemble classifiers on the augmented DET space is explained.•Several evolutionary multiobjective algorithms are exploited to find sparse ensemble classifiers with good performance.•An adaptive MOSEL classifier selection method was designed to select the most suitable classifier for a given dataset.