Granular Ball-Guided Disambiguation for Partial Multilabel Feature Selection via Maximum Consistency Minimum Uncertainty

Partial multilabel feature selection (PMLFS) is a prevalent subject that aims to enhance the performance of multilabel learning (MLL) in the context of noisy labels. In PMLFS, a crucial aspect is handling the false positive labels hidden in the candidate label set, as the imprecise annotations could mislead the feature selection process. However, many existing approaches for partial label disambiguation rely on topology information and tend to be error-prone. Besides, feature selection frameworks are often built upon a linear regression model, leading to a reliance on the classifier and a deficiency in exploring local structures. Focusing on the issues above, this article proposes a novel two-stage PMLFS method, resorting to the ideology of granular computing. In the first stage, a label disambiguation method is developed using label-specific information. Specifically, a specific granular ball computing model is designed to characterize the distribution of datapoints labeled differently, and therefore, using the affinity relationships among samples and balls, the label-specific information concealed in the data distribution can be captured for label disambiguation. In the second stage, a filter-based feature selection method that explores the local structure of samples is presented. This method relies on a devised fuzzy decision neighborhood rough set (FDNRS) to capture more detailed membership information by maximizing the neighborhood consistency of samples' related labels. Simultaneously, the feature selection method minimizes the uncertainty derived from unrelated labels. Extensive experiments on 12 datasets in terms of four evaluation metrics demonstrated the effectiveness of the proposed approach.