| 郭政,赵梅,胡长青.一种有效降维的特征选择方法及其在水声目标识别中的应用[J].声学技术,2021,40(1):14~20 |
| 一种有效降维的特征选择方法及其在水声目标识别中的应用 |
| An effective dimensionality reduction feature selection method and its application in underwater acoustic target recognition |
| 投稿时间：2020-07-20 修订日期：2020-08-15 |
| DOI：10.16300/j.cnki.1000-3630.2021.01.003 |
| 中文关键词: 特征选择 水声目标识别 支持向量机 递归特征消除 猫群算法 |
| 英文关键词: feature selection underwater acoustic target recognition support vector machine (SVM) recursive feature elimination (RFE) cat swarm optimization (CSO) |
| 基金项目:水声对抗技术重点实验室开放基金（JCKY2020207CH02） |
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| 中文摘要: |
| 为在保证目标识别准确率基础上进行有效特征降维，文章以目标识别准确率为特征选择准则，提出一种支持向量机递归特征消除（Support Vector Machine Recursive Feature Elimination，SVM-RFE）快速筛选出部分优质特征子集与猫群算法（Cat Swarm Algorithm，CSO）迭代寻优结合的特征选择方法，并将该方法应用于水声目标识别的特征选择。实验数据处理结果表明：相比SVM-RFE和CSO特征选择算法，文中提出的方法在平均特征维数降低8%的基础上，平均目标识别率提高了1.88%，能够实现有效降维的目的。该方法对判断特征是否适合用于特定的目标识别也有一定应用价值。 |
| 英文摘要: |
| In order to reduce the dimension of feature effectively on the basis of ensuring the accuracy of target recognition, a feature selection method based on combining support vector machine recursive feature elimination (SVM-RFE) algorithm and cat swarm optimization (CSO) algorithm is proposed in this paper. The method is applied to feature selection of underwater acoustic target recognition. Experimental data processing results show that:compared with SVM-RFE and CSO feature selection algorithms, the average feature dimension of the proposed method is reduced by 8%, and the average target recognition rate is improved by 1.88%. This method also has a certain application value in judging whether the feature is suitable for specific target recognition or not. |
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