基于量子进化算法的包装式特征选择方法

针对监督分类中的特征选择问题, 提出一种基于量子进化算法的包装式特征选择方法. 首先分析了现有子集评价方法存在过度偏好分类精度的缺点, 进而提出基于固定阈值和统计检验的两种子集评价方法. 然后改进了量子进化算法的进化策略, 即将整个进化过程分为两个阶段, 分别选用个体极值和全局极值作为种群的进化目标. 在此基础上, 按照包装式特征选择遵循的一般框架设计了特征选择算法. 最后, 通过15个UCI数据集分别验证了子集评价方法和进化策略的有效性, 以及新方法相较于其它6种特征选择方法的优越性. 结果表明, 新方法在80%以上的数据集上取得相似甚至更好的分类精度, 在86.67%的数据集上选择了特征个数更小的子集.

Wrapper Method for Feature Selection Based on Quantum-inspired Evolutionary Algorithm

This study proposes a wrapper method based on a quantum-inspired evolutionary algorithm for feature selection in supervised classification. Firstly, it analyzes the shortcoming of excessively preferring classification accuracy in existing subset evaluation methods and then puts forward two new subset evaluation methods respectively based on a fixed threshold and a statistical test. Second, some improvements are made to the evolutionary strategy of the quantum-inspired evolutionary algorithm. More specifically, its whole evolutionary process is divided into two phases, in which individual and global extrems are selected as the evolutionary target of population respectively. On this basis, a feature selection algorithm is designed in accordance with the general wrapper framework. Finally, 15 UCI datasets are used to validate the effectiveness of the subset evaluation methods and the evolutionary strategy, as well as the superiority of the proposed method over other 6 feature selection methods. The results show that the new wrapper method achieves similar or even better classification accuracy in more than 80% of the datasets and selects feature subset with less number of features in 86.67% of the datasets.