Multi-label causal generic attribute learning algorithm based on causal inference
Similar to causal relationships that commonly exist in the world,causal effect also exists between features and labels.Based on this,a causal inference-based label-specific features learning algorithm CLSF(Causal Label-Specific Features Learning)is proposed in this paper.Label-specific features learning algorithm fully explores the unique characteristics of each label,classifiers trained with label-specific features usually gain an improved performance to a certain extent.Euclidean distance and L1-Norm are the most common methods for existing label-specific features learning algorithms.However,Euclidean distance is insufficient for measuring high-dimension data.Meanwhile,L1-Norm requires a proper parameter election.Therefore,in this paper,we utilize the causal inference method to learn label-specific features.First,we embed the original label into a new space to acquire continuous labels.Then the causal relationship between features and labels is obtained with a causal inference method.Finally,it constrains the original feature space with the learned causality to extract causal label-specific features.Experiments conducted on multiple multi-label benchmark datasets demonstrated that the CLSF algorithm has some advantages over other comparison algorithms.The statistical hypothesis testing results also further prove the proposed method’s effectiveness.
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