A Novel Ensemble Label Propagation With Fuzzy C‐Means and Sorting Empowerment Strategy for Multi‐Label Learning

Recently, Multi‐label learning has emerged as a crucial technique across various fields. However, it typically requires a large amount of labelled data for effective model training. To address this limitation, many researchers have introduced the label propagation (LP) algorithm from semi‐supervised learning into multi‐label learning. Unfortunately, conventional LP approaches only transform multi‐label problem into single‐label formulations, thereby ignoring label correlations. This paper proposes a new LP algorithm integrating fuzzy C‐means (FCM) and sorting empowerment for multi‐label learning, termed C lustering‐based S orting Empowerment L abel P ropagation (CSLP). Firstly, FCM is employed to strengthen the relationship among labels, ensuring that the probability results of the LP algorithm in multi‐label scenarios are more consistent with actual data distributions. Afterward, sorting empowerment, inspired by group decision theory, is adopted as an ensemble strategy to enhance the robustness of the LP algorithm. Finally, extensive experiments on seven multi‐label datasets demonstrate that CSLP outperforms competing algorithms across four evaluation metrics (Hamming loss, Average precision, Recall score, and MicroF1).