Pattern Recognition Classifiers: Fisher and Bayes Classifiers with MATLAB Implementation

This article discusses pattern recognition classifiers implemented in MATLAB, specifically focusing on Fisher and Bayes classifiers applied to gender classification tasks. These classifiers operate by analyzing and learning from training data, then utilizing the acquired knowledge to classify new samples. The Fisher classifier employs statistical principles to differentiate between classes by calculating inter-class variances and maximizing class separability through linear discriminant analysis. In MATLAB implementation, this typically involves computing scatter matrices and solving eigenvalue problems to determine the optimal projection direction. The Bayes classifier, based on Bayesian theorem, categorizes samples into different classes by calculating posterior probabilities using prior probabilities and conditional probabilities for each class. The implementation requires estimating probability density functions from training data and applying Bayes' decision rule to select the class with the highest posterior probability as the classification result. In MATLAB, this often involves using probability distribution fitting functions and implementing Bayesian decision boundaries. Through these classifiers, we can effectively analyze and understand gender-specific characteristics, achieving more accurate classification results. The MATLAB code typically includes data preprocessing, feature extraction, classifier training using fitcdiscr for Fisher and fitcnb for Naive Bayes, and performance evaluation through confusion matrices and cross-validation techniques.