MATLAB Implementation of mRMR (Minimum Redundancy Maximum Relevance) Feature Selection Algorithm

The mRMR (minimum redundancy maximum relevance) algorithm serves as a powerful feature selection method in machine learning workflows. This MATLAB implementation provides researchers and practitioners with an efficient toolkit for applying this sophisticated feature selection technique. The program employs mutual information calculations to evaluate feature importance, maximizing relevance to the target variable while minimizing redundancy among selected features. Key implementation features include optimized matrix operations for handling large datasets, efficient mutual information computation using probability density estimation, and sequential forward selection methodology. The code structure allows users to specify the number of features to select, configure mutual information parameters, and handle both discrete and continuous variables through appropriate discretization methods. Users can leverage this implementation to significantly improve model performance by selecting optimal feature subsets that enhance predictive accuracy while reducing computational complexity. The algorithm's effectiveness has been demonstrated across diverse domains including bioinformatics for gene selection, image processing for feature extraction, and natural language processing for text classification tasks. The MATLAB implementation includes functions for data preprocessing, feature ranking visualization, and performance validation metrics. Practical code examples demonstrate how to integrate mRMR feature selection with common machine learning pipelines, making it particularly valuable for researchers working with high-dimensional data. This comprehensive tool represents an essential resource for advancing feature selection research and applications in machine learning projects.