Open Access

ARTICLE

Causality-Driven Common and Label-Specific Features Learning

Yuting Xu^1,*, Deqing Zhang¹, Huaibei Guo², Mengyue Wang¹

1 School of Intelligent Transportation Modern Industry, Anhui Sanlian University, Hefei, 230601, China
2 Heyetang Middle School, Jinhua, 322010, China

* Corresponding Author: Yuting Xu. Email:

Journal on Artificial Intelligence 2024, 6, 53-69. https://doi.org/10.32604/jai.2024.049083

Received 27 December 2023; Accepted 04 March 2024; Issue published 05 April 2024

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.

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Keywords

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