SVM KNN Classification Algorithm Implementation in MATLAB

In this article, we will discuss in detail the k-nearest neighbors classification algorithm based on Support Vector Machine (SVM). SVM is a widely used supervised learning model employed for both classification and regression analysis. Its fundamental concept involves mapping low-dimensional input data into a high-dimensional feature space through kernel functions, enabling effective linear separation in this transformed space. The k-nearest neighbors algorithm is an instance-based learning method that predicts labels for new data points by considering the majority labels among the k closest training examples in the feature space.

For MATLAB implementation, the SVM-KNN hybrid approach typically involves two key phases: First, training an SVM model using functions like fitcsvm() to establish optimal decision boundaries with parameters such as kernel type (linear, RBF, polynomial) and regularization constant C. Second, applying the k-NN algorithm within the SVM-defined feature space using functions like fitcknn(), where critical parameters include the number of neighbors (k), distance metric (Euclidean, Manhattan), and voting scheme. This combined methodology leverages SVM's strong generalization capability for boundary definition and k-NN's flexibility in handling complex decision regions, resulting in improved classification accuracy particularly for overlapping class distributions.

The algorithm's MATLAB implementation would typically involve preprocessing data with normalization functions like zscore(), followed by SVM model training with cross-validation using crossval() to optimize hyperparameters. The k-NN component would then operate on the SVM-transformed features, where developers can implement custom distance calculations using pdist2() function and majority voting logic through mode() function. This integration demonstrates how combining theoretical strengths of both algorithms can achieve superior performance compared to individual methods, especially when dealing with non-linearly separable datasets.