Dynamic Feature Selection Based on F-fuzzy Rough Set for Label Distribution Learning

Label distribution learning(LDL) has been received widespread attention as an effective learning paradigm in the field of data mining. However, existing feature selection algorithms for LDL are performed under static conditions or semi-dynamic conditions, and the dynamic information of data is not considered sufficiently. To address these issues, a feature selection algorithm for LDL under dynamic conditions is proposed, which takes full use of dynamic information from data. Specially, a novel rough set model called F-double-fuzzy rough set is created to handle dynamic LDL data, which is extended from F-fuzzy rough set. Then, F-fuzzy-condition entropy is defined to fuse information together in F-double-fuzzy rough set, which is considered as a measure for feature selection. Thirdly, a dynamic feature selection algorithm for LDL is proposed with the F-fuzzy-condition entropy. Fourthly, a novel method of covering set is proposed to simulate dynamic LDL data, and its merits are explained intuitively. At last, experiments on 14 datasets with six widely used metrics demonstrate that the performance of the proposed algorithm has advantages over the state-of-the-art algorithms.