Confidence-Induced Granular Partial Label Feature Selection via Dependency and Similarity

Partial label learning (PLL) tackles scenarios where the unique ground-truth label of each sample is concealed within a candidate label set. Dimensionality reduction, considering labeling confidence estimation, has become a promising strategy to enhance the generalization performance of PLL models. However, current studies achieve dimensionality reduction, often relying on kNN-based labeling confidence estimation or disregarding potential labeling information. To address this issue, this paper proposes a novel Confidence-induced granular Partial label feature selection method using Dependency and Similarity (CPDS), which consists of two phases: Labeling Confidence Estimation (LCE) and Feature Selection (FS). For LCE, through granular ball computing, the feature space's similarity and the label space's correlation between the training data and the granular ball can be fused simultaneously, thereby effectively reconstructing more credible labeling confidence from candidate labels with more diverse semantic representation information. In the FS stage, by leveraging the LC with more diverse information, the proposed PLL neighborhood decision system further effectively combines feature dependency and label similarity to identify a feature subset with more discriminative capabilities, thereby achieving better performance for classification tasks. Among them, feature dependency effectively utilizes the dependency between neighborhoods and equivalence relations, while label similarity fully exploits the similarity between each sample and its neighbors. Extensive experiments show that CPDS significantly outperforms the compared approaches in most cases on nine controlled UCI datasets and five real-world datasets, demonstrating the superiority of the proposed method.