Generalized Rough Set Method for Ensemble Feature Selection and Multiple Classiffier Fusion
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摘要: 为改善多分类器系统的分类性能, 提出了基于广义粗集的集成特征选择方法. 为在集成特征选择的同时获取各特征空间中的多类模式可分性信息, 研究并提出了关于多决策表的相对优势决策约简, 给出了关于集成特征选择的集成属性约简 (Ensemble attribute reduction,EAR) 方法, 结合基于知识发现的 KD-DWV 算法进行了高光谱遥感图像植被分类比较实验. 结果表明, EAR 方法与合适的多分类器融合算法结合可有效提高多分类器融合的推广性.Abstract: For improving the performance of multiple classifier system, a novel method of ensemble feature selection is proposed based on generalized rough set. In the paper, the relative dominance decision reduct (RDDR) with respect to multiple decision tables is presented to obtain the best feature subsets and interclass separability from different feature spaces. Then, the ensemble attribute reduction (EAR) method is proposed for ensemble feature selection. Using the KD-DWV algorithm based on knowledge discovery, the effectiveness of EAR was examined with the vegetation classification on a hyperspectral image. The result of the comparison experiment shows that EAR can be used to improve the generalization of multiple classifier system by combining appropriate multiple classifier fusion algorithm.
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