关键词: 多标签分类, Logistic回归, k近邻, Markov边界

Abstract: In multi-label classification, this paper constructs the new dataset about the nearest neighbors sample class mark through the classification idea of the k-nearest neighbors. The multi-label classification algorithm are established on the new dataset through the regression model. Firstly, this paper calculates the k-nearest neighbors distance of the test samples in each label and constructs new dataset of each sample on the label set. Secondly, the multi label classification algorithm is given based on sample k-nearest neighbors dataset, using linear regression and Logistic regression. In order to further exploit the information of original dataset, considering the Markov boundary of the original property each label and combining the feature of the new dataset to establish a new regression model, a multi-label classification algorithm about Markov boundary is proposed. The experimental results show that the multi-label learning method is better than the common learning algorithm.

Key words: multi-label classification, Logistic regression, k-nearest neighbors, Markov boundary