Optimization of the aggregation in AdaBoost algorithm by particle swarm optimization and its application in classification problems

摘要

In this paper, particle swarm optimization (PSO) algorithm and Adaptive Boosting (AdaBoost) algorithm are combined to form a hybrid learning algorithm PSO-AB to boost the classification ability of support vector machine (SVM). This hybrid adopts SVM to classify the experimental data, uses AdaBoost algorithm to boost the classification results, and then uses PSO to optimize the boosted results. Experimental results of two clinical data show that AdaBoost algorithm could improve the accuracy of training set extremely, but for the testing set the result is not satisfactory. PSO-AB makes it possible to maximize the testing accuracy of AdaBoost algorithm on the premise of that the accuracy of training set is still exact, and will be a more effective method to classification problems compared to AdaBoost algorithm.