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Using bidirectional binary particle swarm optimization for feature selection in feature-level fusion recognition system (EI CONFERENCE)

文献类型：会议论文

作者Wang D.; Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Y.
出版日期2009
会议名称2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009, May 25, 2009 - May 27, 2009
会议地点Xi'an, China
关键词In feature-level fusion recognition system the other is optimizing system sensor design to get outstanding cost performance. So feature selection become usually necessary to reduce dimensionality of the combination of multi-sensor features and improve system performance in system design. In general there are two main missions. One is improving the recognition correct rate as soon as possible the optimization is usually applied to feature selection because of its computational feasibility and validity. For further improving recognition accuracy and reducing selected feature dimensions this paper presents a more rational and accurate optimization Bidirectional Binary Particle Swarm Optimization (BBPSO) algorithm for feature selection in feature-level fusion target recognition system. In addition we introduce a new evaluating function as criterion function in BBPSO feature selection method. At the last we utilized Leave-One-Out method to validate the proposed method. The experiment results show that the proposed algorithm improves classification accuracy by two percentage points while the selected feature dimensions are less one dimension than original Particle Swarm Optimization approach with 16 original feature dimensions. 2009 IEEE.
页码3810-3814
收录类别EI
源URL[http://ir.ciomp.ac.cn/handle/181722/33319]  
专题长春光学精密机械与物理研究所_中科院长春光机所知识产出_会议论文
推荐引用方式
GB/T 7714		 Wang D.,Wang Y.,Wang Y.,et al. Using bidirectional binary particle swarm optimization for feature selection in feature-level fusion recognition system (EI CONFERENCE)[C]. 见:2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009, May 25, 2009 - May 27, 2009. Xi'an, China.

入库方式： OAI收割

来源：长春光学精密机械与物理研究所

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