AdaBoost Ensemble Learning Algorithm with MATLAB Code Implementation

This article presents a MATLAB implementation of the AdaBoost ensemble learning algorithm. AdaBoost is a widely-used machine learning algorithm designed to enhance classifier performance through iterative training of multiple weak learners. The algorithm operates by sequentially training weak classifiers and combining them into a strong classifier. During each iteration, the weights of misclassified instances are increased, forcing subsequent classifiers to focus on previously challenging patterns. The MATLAB implementation typically involves key components such as: initializing sample weights, training weak classifiers (often decision stumps), calculating classifier error rates, updating instance weights using exponential loss, and combining weak classifiers through weighted voting. The code structure generally includes functions for data preprocessing, iterative training loops, and prediction modules. Practical applications of this implementation include classification tasks like face recognition, text categorization, and biomedical pattern detection. Understanding AdaBoost's theoretical foundations and mastering its MATLAB implementation enables effective application of this powerful ensemble method in various machine learning projects.