Dynamic interaction feature selection based on fuzzy rough set

Feature selection is an important data preprocessing approach that continues to be concerned in data mining. It has been extensively used to construct learning models and reduce storage and computing requirements. Fuzzy rough set is a useful theoretical tool for dealing with the mixed data with fuzziness and inconsistency. Hence, feature selection based on fuzzy rough sets has attracted much attention. However, most of the existing studies ignore the interaction between features, which leads to the loss of useful information. Motivated by this issue, we devise a Dynamic Interaction Feature Selection method based on Fuzzy Rough Set (DIFS_FRS). The method simultaneously considers the interactive relation between features, the relation between conditional features and decision classes, and the dynamic change of feature weights with the variation of feature subset. Firstly, the single-level dependency relevancy between features and classes is defined by the fuzzy dependency degree. Secondly, the multi-level joint interaction between features about classes is investigated. Correspondingly, the correlation evaluation index of features is constructed. Thereafter, a dynamic updating-feedback mechanism is established for a novel feature evaluation function. Finally, compared with the other six representative algorithms on eighteen data sets, the DIFS_FRS algorithm is demonstrated to have better performance.