Decision Boundary Identification Using Minimum Distance Classification

By employing the minimum distance classification method, MATLAB programs can be implemented to identify decision boundaries during classification discrimination. This approach enables the discrimination between two known classes of training samples while facilitating analysis of classification error rates. The algorithm computes Euclidean distances between test samples and class centroids, assigning samples to the class with the minimum distance. This method effectively categorizes different samples and delivers accurate classification results. Through functions like pdist2 for distance calculation and k-nearest neighbor implementation, the classification accuracy and reliability can be further enhanced by increasing the quantity and diversity of training samples. Additionally, to improve classification performance, alternative algorithms such as Support Vector Machines (SVMs) or parameter optimization techniques can be incorporated, enabling more comprehensive analysis and precise recognition outcomes through systematic cross-validation and confusion matrix evaluation.