MRMR和relieff特征选择方法

资 源 简 介

MRMR和relieff特征选择方法，很经典的，简单易用！

详 情 说 明

The MRMR and Relief-F feature selection methods are two very classic and commonly used techniques. These methods are essential in reducing the number of features in a dataset and selecting the most relevant ones for analysis. MRMR, which stands for "minimum redundancy maximum relevance," works by minimizing the redundancy between features while maximizing their relevance to the target variable. On the other hand, Relief-F, which is short for "relief feature selection," is a distance-based algorithm that assesses the relevance of features by measuring the difference in feature values between samples. Despite their simplicity and ease of use, these methods have proven to be highly effective in a wide range of applications, including bioinformatics, image analysis, and text classification.