It is an open issue how to generate base classifiers with higher diversity and accuracy for ensemble learning.In this paper

a novel algorithm is proposed to solve this problem---particle swarm optimization is used to search for an optimal feature weight distribution which makes the classification error rate of training data sample by the distribution in AdaBoost minimal.Then

the feature subspace is constructed according to the optimal feature weight distribution

which is applied into the training process of AdaBoost.Thus

the accuracy of base classifier is advanced;meanwhile

the diversity between classifiers is improved.Finally

majority voting method is utilized to fuse the base classifiers' results and experiments have been done to attest the validity of the proposed algorithm.