@Article{jai.2024.049083,
AUTHOR = {Yuting Xu, Deqing Zhang, Huaibei Guo, Mengyue Wang},
TITLE = {Causality-Driven Common and Label-Specific Features Learning},
JOURNAL = {Journal on Artificial Intelligence},
VOLUME = {6},
YEAR = {2024},
NUMBER = {1},
PAGES = {53--69},
URL = {http://www.techscience.com/jai/v6n1/56063},
ISSN = {2579-003X},
ABSTRACT = {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.},
DOI = {10.32604/jai.2024.049083}
}