MATLAB Source Code for SBS and SFS Feature Extraction to Optimize Multidimensional Vector Selection

This document presents MATLAB source code implementing Sequential Backward Selection (SBS) and Sequential Forward Selection (SFS) feature extraction algorithms for identifying optimal subsets from multidimensional vectors. The implementation typically involves wrapper methods that evaluate feature subsets using criteria like classification accuracy or information gain, with SBS starting from the full feature set and recursively removing features, while SFS begins with an empty set and incrementally adds features. However, these algorithms face practical challenges including dataset dimensionality, feature correlations, and computational complexity. The code may include cross-validation routines to handle overfitting and scoring functions to assess feature subset quality. For improved results, researchers should consider hybrid approaches like floating search methods or explore alternative feature selection techniques such as filter methods (e.g., mutual information) or embedded methods (e.g., L1 regularization). Alternative implementations in Python using scikit-learn's SequentialFeatureSelector or R's FSelector package could provide comparative advantages in specific scenarios. While multiple aspects require optimization—including handling high-dimensional data and computational efficiency—continuous experimentation with different evaluation metrics and algorithm modifications can yield enhanced feature selection performance.