What is UCI Machine Learning Repository

The aim of this chapter is to propose a hybrid classification algorithm based on particle swarm optimization (PSO) to enhance the generalization performance of the adaptive boosting (AdaBoost) algorithm. AdaBoost enhances any given machine learning algorithm performance by producing some weak classifiers which requires more time and memory and may not give the best classification accuracy. For this purpose, PSO is proposed as a post optimization procedure for the resulted weak classifiers and removes the redundant classifiers. The experiments were conducted on the basis of ionosphere data set, thoracic surgery data set, blood transfusion service center data set (btsc) and Statlog (Australian credit approval) data set. The experimental results show that a given boosted classifier with post optimization based on PSO improves the classification accuracy for all used data. Also, the experiments show that the proposed algorithm outperforms other techniques with best generalization.