MATLAB Implementation of Adaboost_M1 Algorithm

This article presents a MATLAB implementation of the Adaboost_M1 algorithm, primarily used for integrating diverse classifiers to enhance performance. Adaboost_M1 operates as a Boosting algorithm whose core mechanism involves iteratively training multiple weak classifiers and combining them into a strong classifier. The implementation typically includes key components such as weight initialization for training samples, iterative classifier training with weight updates, and a final weighted voting mechanism. Through successive iterations, the algorithm adjusts sample weights to focus on previously misclassified instances, while the final classification decision combines all weak classifiers' outputs with their respective confidence weights. This methodology significantly improves classification accuracy and robustness, with the MATLAB code typically featuring functions for data preprocessing, weak learner training (such as decision stumps), and performance evaluation metrics. The algorithm's effectiveness lies in its ability to reduce both bias and variance while handling complex classification boundaries through ensemble learning principles.