Abstract: Determining the classification of the test sample by using neighbors’ labels achieves good results in multiple label classification．The mapping relationships of these algorithms are established between the labels of training examples and the number of different samples in their k-nearest neighbors by learning from the training set．The label of a test sample can be easily predicted by applying the mapping relationship．The disadvantage of these algorithms is to consider only the mapping relationship between the labels of the test examples and the number of different samples in their k-nearest neighbors,and to ignore the local correlation between the labels of the test examples and their k-nearest neighbors．This paper proposed an algorithm called ML-WKNN algorithm,which classifies the test examples through the mapping relationship between the labels of the training examples and their k-nearest neighbors by considering the local correlation between the labels of the training examples and their k-nearest neighbors．The experimental results show that the ML-WKNN algorithm achieves better results than other algorithms in dealing with the multi-label classification problems and automatic image annotation.

Key words: Multi-label learning,KNN,Classification,Local correlation