In machine learning, feature selection is a kind of important dimension reduction techniques, which aims to choose features with the best discriminant ability to avoid the issue of curse of dimensionality for subsequent processing. As a supervised feature selection method, Fisher score (FS) provides a feature evaluation criterion and has been widely used. However, FS ignores the association between features by assessing all features independently and loses the local information for fully connecting within-class samples. In order to solve these issues, this paper proposes a novel feature evaluation criterion based on FS, named iteratively local Fisher score (ILFS). Compared with FS, the new criterion pays more attention to the local structure of data by using K nearest neighbours instead of all samples when calculating the scatters of within-class and between-class. In order to consider the relationship between features, we calculate local Fisher scores of feature subsets instead of scores of single features, and iteratively select the current optimal feature to achieve this idea like sequential forward selection (SFS). Experimental results on UCI and TEP data sets show that the improved algorithm performs well in classification activities compared with some other state-of-the-art methods.

在机器学习中，特征选择是一种重要的降维技术，其目的是选择具有最佳判别能力的特征，以避免在后续处理中出现维数诅咒的问题。作为一种有监督的特征选择方法，Fisher评分（FS）提供了一种特征评估标准，并已被广泛使用。但是，FS通过独立评估所有特征而忽略了特征之间的关联，并且丢失了用于完全连接类内样本的本地信息。为了解决这些问题，本文提出了一种基于FS的新颖特征评估准则，即迭代局部Fisher评分（ILFS）。与FS相比，新准则使用K更加关注数据的局部结构计算类内和类间散布时，最接近的邻居而不是所有样本。为了考虑特征之间的关系，我们计算特征子集的局部Fisher分数，而不是单个特征的分数，并迭代选择当前的最佳特征以实现此想法，例如顺序前向选择（SFS）。在UCI和TEP数据集上的实验结果表明，与其他一些最新方法相比，改进的算法在分类活动中表现良好。