AdaBoost performance improvement using PSO algorithm

Abstract

An improved AdaBoost algorithm based on optimizing search in sample space is presented. Working with data in large scale need more time to compare samples for finding a threshold in the AdaBoost algorithm when using decision stump as a weak classifier. We used PSO algorithm to evolve and select best feature in sample space for a weak classifier to reduce time. The experiment results show that with applying PSO to the decision stump, time consuming of the AdaBoost algorithm has been improved than base Adaboost. As a result, using evolutionary algorithms in such problems which have large scale, can reduce searching time for finding best solution and increase performance of algorithms in hand.