Partial label feature selection via label disambiguation and neighborhood mutual information

Partial label learning aims to learn from training instances, each of which is associated with a set of candidate labels but only one is a ground-truth label. Feature selection is an effective method to improve the generalization capability of the learning model; however, partial label feature selection work is exceptionally challenging due to the limitation and ambiguity of label information. Therefore, this paper proposes a partial label feature selection algorithm based on label disambiguation and neighborhood mutual information. Firstly, neighborhood granularity is utilized to determine the neighborhoods of instances to disambiguate the candidate labels. Secondly, based on label confidence induced by disambiguation, feature relevance and redundancy are measured by neighborhood mutual information, which avoids the negative impact of data discretization on feature selection and directly handles continuous features. Concurrently, the kappa coefficient is employed to estimate the label consistency for describing the influences of feature changes on the label space. Then, the significance of each feature is evaluated by fusing feature relevance, feature redundancy, and label consistency. Finally, the effectiveness of the proposed algorithm is verified by comparing the proposed algorithm with four base classifiers and other feature selection methods. Furthermore, the feasibility of the proposed disambiguation method is demonstrated through comparison with four state-of-the-art disambiguation methods.