How to accurately measure the relevance and redundancy of features is an
age-old challenge in the field of feature selection. However, existing
filter-based feature selection methods cannot directly measure redundancy for
continuous data. In addition, most methods rely on manually specifying the
number of features, which may introduce errors in the absence of expert
knowledge. In this paper, we propose a non-parametric feature selection
algorithm based on maximum inter-class variation and minimum redundancy,
abbreviated as MVMR-FS. We first introduce supervised and unsupervised kernel
density estimation on the features to capture their similarities and
differences in inter-class and overall distributions. Subsequently, we present
the criteria for maximum inter-class variation and minimum redundancy (MVMR),
wherein the inter-class probability distributions are employed to reflect
feature relevance and the distances between overall probability distributions
are used to quantify redundancy. Finally, we employ an AGA to search for the
feature subset that minimizes the MVMR. Compared with ten state-of-the-art
methods, MVMR-FS achieves the highest average accuracy and improves the
accuracy by 5% to 11%.

基于最大类间差异和最小冗余的非参数特征选择算法 (MVMR-FS)，通过引入监督和无监督的核密度估计，利用概率分布反映特征相关性和冗余性，在十种最先进方法中取得最高平均准确率，并提高 5% 至 11% 的准确率。