MATLAB Implementation of Naive Bayes Classification Algorithm

The following presents the source code for implementing Naive Bayes classification using MATLAB. This code demonstrates the fundamental principles and implementation approach of the Naive Bayes algorithm, including probability estimation and classification decision making. Before using this code, please ensure you have MATLAB software installed on your system.

%% Naive Bayes Classifier

function [output] = NaiveBayesClassifier(input)

% input: Input data matrix with dimensions [n * m], where n represents the number of samples and m represents the number of features

% output: Classification results vector with dimensions [n * 1] containing predicted class labels

% Implementation workflow includes: calculating prior probabilities, estimating feature likelihoods, computing posterior probabilities using Bayes' theorem, and making classification decisions based on maximum posterior probability

% Key algorithm steps:

% 1. Calculate class prior probabilities from training data

% 2. Estimate conditional probabilities for each feature given each class

% 3. Apply Bayes' theorem: posterior = prior * likelihood / evidence

% 4. Implement the naive independence assumption between features

% 5. Classify samples by selecting the class with highest posterior probability

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This implementation demonstrates the complete workflow of a basic Naive Bayes classifier, highlighting the core mathematical concepts and their practical application in MATLAB. The code provides insights into handling continuous and discrete features, probability smoothing techniques, and efficient matrix operations for optimal performance.