An accelerator and feature selection using fuzzy information granularity to partially labeled data

Fuzzy rough set, as an important mathematical method, plays a vital role in feature selection of numerical data. Most existing fuzzy rough set-based feature selection methods merely consider a single-type feature or are mainly designed for datasets with complete decision labels. Additionally, since the acquisition of object labels requires expensive time and resource costs, only some objects possess decision labels. Partially labeled hybrid data consisting of symbolic, numerical and missing features are often more common in practical applications. In this study, we propose a feature selection method based on fuzzy information granularity for partially labeled hybrid data, which encompasses three key designs. Firstly, an improved fuzzy decision strategy based on the neighborhood granule is proposed to incorporate the potential decision label besides complete decision labels. Secondly, a monotonic fuzzy information granularity measure is designed to simultaneously assess the fuzziness and uncertainty of feature subsets in partially labeled hybrid data. Based on this, a heuristic feature selection algorithm is proposed to select the optimal feature subset. Thirdly, an accelerator mechanism is proposed to progressively shrink the object space to further reduce the running time of the proposed feature selection algorithm. The extensive experimental results demonstrate that the proposed method improves classification performance while reducing time cost.