基于蚁群优化的特征选择新方法

利用蚁群优化算法解决特征选择问题，以获得能代表问题空间的较优特征子集，并能降低分类系统的搜索空间。以航空纹理影像的特征选择和分类问题为例，利用主分量变换和蚁群优化算法分别对原始纹理影像特征集合进行特征提取、选择和分类。结果表明，本文方法不仅能够降低图像特征空间维数，减少图像分类的工作量，而且还可以提高分类识别的正确率。

A Novel Approach for Feature Selection Based on Ant Colony Optimization Algorithm

A novel approach is presented to solve feature subset selection based on ACO(ant colony optimization algorithm).The approach has the ability to accommodate multiple criteria such as accuracy and cost of classification into the process of feature selection and find the effective feature subset for texture classification.A classifier based on minimum distance is described to classify two types of texture images with feature subset selected by ACO and extracted by PCA(principal component analysis) respectively.Experimental result illustrates that the algorithm can reduce feature dimension,speed the classification of image and improve the recognition rate compared to PCA.