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论文:2014,Vol:32,Issue(3):362-367 |
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引用本文： |
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杨宏晖, 王芸, 戴健. 水下目标识别中样本选择与SVME融合算法[J]. 西北工业大学 |
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Yang Honghui, Wang Yun, Dai Jian. Instance Selection and SVM Ensembles for Underwater Acoustic Target Recognition[J]. Northwestern polytechnical university |
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| 水下目标识别中样本选择与SVME融合算法 |
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| 杨宏晖, 王芸, 戴健 |
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| 西北工业大学 航海学院, 陕西 西安 710072 |
| 摘要: |
| 水下目标识别中训练样本集含有冗余样本、噪声样本及无关样本,且特征提取、特征选择和决策系统设计过程分离而导致系统识别性能的下降,为此提出了基于加权最近邻收缩样本选择的SVM集成算法(SVME-WRNN)和基于加权免疫克隆样本选择的SVM集成算法(SVME-WICISA)。这2种集成方法通过样本选择来构建精度高、差异大的子分类器,并将其集成。利用4类水下目标实测数据进行了分类仿真实验。实验结果表明:SVME-WRNN算法和SVME-WICISA算法与SVME算法(无样本选择)相比较,在识别率相当的情况下,大幅度地降低了训练样本数目,得到的综合分类器具有良好的分类精度。 |
| 关键词:
噪声
算法
决策
特征提取
支持向量机
水声学
样本选择
目标识别
加权免疫克隆样本选择算法
加权最近邻收缩
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| Instance Selection and SVM Ensembles for Underwater Acoustic Target Recognition |
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| Yang Honghui, Wang Yun, Dai Jian |
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| College of Marine Engineering, Northwestern Polytechnical University, Xi'an 710072, China |
| Abstract: |
| Because the training instance set for recognizing underwater acoustic targets contains many noise sam-ples, redundant samples and irrelevant samples, and because the systems for feature extraction, feature selection and decision making are designed separately, the underwater acoustic target recognition performance declines. Hence we propose the SVM ensemble based on weighted reduced nearest neighbor (SVME-WRNN) and the SVM ensemble based on weighted immune clone instance selection algorithm(SVME-WICISA). The ensembles use in-stance selection to build precise and diverse sub-classifiers and then combine them. We simulate the classification of the measurement data of four types of underwater acoustic targets. The simulation results, given in Figs.3, 4 and 5 and Table 3, and their analysis show preliminarily that, compared with the SVME without instance selection, the two ensembles can greatly reduce the number of training instances when their classification accuracy is almost the same and that the combined classifier has satisfactory classification accuracy. |
| Key words:
acoustic noise
algorithms
decision making
feature extraction
support vector machines
underwater acoustics
instance selection
target recognition
weighted immune clone instance selection algorithm
weighted reduced nearest neighbor
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| 收稿日期: 2013-10-17
修回日期:
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| DOI: |
| 基金项目: 国家自然科学基金(60672136);水下测控技术重点实验室基金(9140C260505120C26104)资助 |
| 通讯作者:
Email： |
| 作者简介: 杨宏晖(1971-),女,西北工业大学副教授、博士,主要从事声信号处理及模式识别的相关研究。
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| 作者相关文章 |
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| 杨宏晖 在本刊中的所有文章 |
| 王芸 在本刊中的所有文章 |
| 戴健 在本刊中的所有文章 |
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参考文献: |
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