In order to improve classification performance and exploit label correlations,AdaBoost.MLR algorithm was proposed.Cosine similarity was adopted to capture the complex correlations among labels in AdaBoost.MLR algorithm,a supplementary label matrix was incorporated,which augments the incomplete original label matrix by exploiting the label correlations,label space was divided into three parts of label set,relevant label set and irrelevant label set,weight update rule was modified according to correlations among labels and the results of weak learner.AdaBoost.MLR algorithm was able to solve multi class classification problem specially,label similarity matrix,instead of cosine similarity,was constructed by the classification results of temporary strong learner combined by previous trained weak learners.The experimental results illustrated that the proposed algorithm was superior to existing algorithms,and the classification performance was improved significantly on datasets had complex correlations among labels.
