A framework of multiple kernel ensemble learning for classification using two-stage feature selection method

作者：Qi, Chengming^*; Hu, Lishuan; Yu, Xin

来源：Journal of Intelligent and Fuzzy Systems, 2017, 33(5): 2737-2747.

DOI：10.3233/JIFS-169323

• Feature selection
• hyperspectral classification
• Jeffries-Matusita measure
• Multiple Kernel Learning
• Mutual Information

Feature selection aims at selecting a feature subset that has the most discriminative information and preserve most of characteristics from original features in HyperSpectral Image (HSI) classification. This paper proposes a two-stage feature selection method based on Mutual Information (MI) and Jeffries-Matusita (J-M) measure. In first stage, we select a feature subset with minimal redundancy maximal relevance criteria. In second stage, we select further a feature subset from which obtained in first stage by maximizing J-M distance. Multiple Kernel Learning (MKL) and Ensemble Learning (EL) are promising family of machine learning algorithms and have been applied extensively in HSI classification. Many MKL methods often formulate the problem as an optimization task. To avoid solving the complicated optimization problem, this paper presents an ensemble learning framework, SMKB (Stochastic Multiple Kernel Boosting), which applies Adaptive Boosting (AdaBoost) and stochastic approach to learning multiple kernel-based classifier for multi-class classification problem. We examine empirical performance of proposed approach on benchmark hyperspectral classification data set in comparison with various state-of-the-art algorithms. Experimental results demonstrate that the proposed method obtains better feature subsets and is more effective and efficient than classical methods.

• 出版日期2017
• 单位北京联合大学