Naive Bayes Classifier Implementation in MATLAB

This article presents two types of Bayesian classifiers implemented in MATLAB: the Naive Bayes classifier and the standard Bayes classifier. In the fields of machine learning and pattern recognition, Bayesian classifiers represent a widely-used classification approach that operates through probabilistic models based on Bayes' theorem. The Naive Bayes classifier stands as the simplest and most frequently employed variant, operating under the assumption that all features are mutually independent, with feature conditional probability distributions following Gaussian distributions for given classes. The MATLAB implementation typically involves calculating prior probabilities from training data and determining Gaussian parameters (mean and variance) for each feature-class combination using functions like fitcnb for model training. The standard Bayes classifier serves as a generalization of the Naive Bayes approach, eliminating the feature independence assumption and thereby capable of handling more complex datasets. This implementation often requires estimating covariance matrices to capture feature relationships, potentially utilizing MATLAB's classification techniques with full covariance matrix calculations. Throughout this article, we provide comprehensive details about the implementation methodologies and application scenarios for both classifiers, including code structure explanations, key function descriptions (such as probability density function calculations and decision boundary determinations), and practical usage examples to help readers better understand and apply these methods in real-world classification tasks.