Sequence Optimization Classifier Chain Based on Label-Specific Features and Causal Discovery

Classifier chain is an important multi-label classification method to mine multi-dimensional label information of specific objects by using the correlation between labels. To solve the problems in the existing classifier chain algorithm, including the redundancy of learning features caused by the base learner training of each label in the complete feature space, and the low efficiency of information utilization among labels caused by the random sequence of label learning and the one-way non-feedback in the training process of classifier chain, a sequence optimization classifier chain based on label-specific features and causal discovery was proposed. In this method, affine propagation clustering was used to construct advanced structured features for each base learner, reducing the difficulty of training single label nodes. At the same time, conditional entropy was used to mine the causal relationship between labels, optimize the learning sequence and improve the utilization density of relevant information between labels. The experimental results on several open datasets show that the sequential optimization classifier chain can effectively enhance the learning effect of single node and the utilization of correlation information between multi-labels, and improve the classification effect of multi-labels, possessing high practical value. © 2021, Editorial Department of Transaction of Beijing Institute of Technology. All right reserved.