Feature Selection Using Branch-and-Bound Method

In pattern recognition applications, the branch-and-bound method is commonly employed for feature selection. This MATLAB-based implementation demonstrates the algorithm through a practical example of selecting two optimal features from six available features. The branch-and-bound method is a widely-used optimization algorithm that efficiently identifies optimal feature combinations from large feature sets through systematic search space partitioning and pruning. The core principle involves significantly reducing the number of candidate feature combinations during the search process, thereby enhancing both efficiency and accuracy in feature selection. The MATLAB code typically implements this through recursive tree traversal, where each node represents a feature subset candidate, with bounding criteria eliminating suboptimal branches early. This makes the branch-and-bound method particularly valuable in pattern recognition domains, offering substantial research and practical applications for optimal feature subset identification.