TY  - EJOU
AU  - Xu, Yuting 
AU  - Zhang, Deqing 
AU  - Guo, Huaibei 
AU  - Wang, Mengyue 

TI  - Causality-Driven Common and Label-Specific Features Learning
T2  - Journal on Artificial Intelligence

PY  - 2024
VL  - 6
IS  - 1
SN  - 2579-003X

AB  - In multi-label learning, the label-specific features learning framework can effectively solve the dimensional catastrophe problem brought by high-dimensional data. The classification performance and robustness of the model are effectively improved. Most existing label-specific features learning utilizes the cosine similarity method to measure label correlation. It is well known that the correlation between labels is asymmetric. However, existing label-specific features learning only considers the private features of labels in classification and does not take into account the common features of labels. Based on this, this paper proposes a Causality-driven Common and Label-specific Features Learning, named CCSF algorithm. Firstly, the causal learning algorithm GSBN is used to calculate the asymmetric correlation between labels. Then, in the optimization, both -norm and -norm are used to select the corresponding features, respectively. Finally, it is compared with six state-of-the-art algorithms on nine datasets. The experimental results prove the effectiveness of the algorithm in this paper.
KW  - Label-specific features learning; causal learning; asymmetric label correlation; common features

DO  - 10.32604/jai.2024.049083