基于混沌搜索的特征选择方法

针对特征选择效率较低的问题，提出了一种基于混沌搜索的特征选择（CSFS）方法。建立候选特征－混沌变量映射模型，将候选特征映射到混沌空间，实现候选特征向量与混沌变量之间的相互转化；利用混沌变量迭代演化进行特征选择；利用分类器对得到的特征向量进行性能评价。以焊缝缺陷特征为例对该特征选择方法进行了有效性验证，并与基于遗传算法的特征选择（GAFS）方法进行了对比。实验结果表明：在获取的特性向量的识别性能相当的情况下，CSFS方法的耗时仅为GAFS方法的61.1%.

Feature Selection Based on Chaos Search

To improve the efficiency of feature selection, a feature selection method based on chaos search (CSFS) is proposed. Firstly, a mapping model for feature candidates and chaotic variables is established，which maps the feature candidates to the chaos space and realizes the interconversion between them. Secondly, the feature selection is carried out by means of the evolution of the chaotic variable. Finally, the classifier is used to evaluate the obtained feature vector. The features of weld defects are taken for example to verify the proposed method, which is compared with a gene-algorithm-based feature selection (GAFS) method. The experimental results demonstrate that the computation time of CSFS is only 61.1% of that by GAFS in the case of obtaining the feature vectors with the same recognition performance.