IFCEM based recognition method for target with interval-overlapped hybrid attributes

Abstract

When the attributes of unknown targets are not just numerical attributes, but hybrid attributes containing linguistic attributes, the existing recognition methods are not effective. In addition, it is more difficult to identify the unknown targets densely distributed in the feature space, especially when there is interval overlap between attribute measurements of different target classes. To address these problems, a novel method based on intuitionistic fuzzy comprehensive evaluation model (IFCEM) is proposed. For numerical attributes, targets in the database are divided into individual classes and overlapping classes, and for linguistic attributes, continuous interval-valued linguistic term set (CIVLTS) is used to describe target characteristic. A cloud model-based method and an area-based method are proposed to obtain intuitionistic fuzzy decision information of query target on numerical attributes and linguistic attributes respectively. An improved inverse weighted kernel fuzzy c-means (IWK-FCM) algorithm is proposed for solution of attribute weight vector. The possibility matrix is applied to determine the identity and category of query target. Finally, a case study composed of parameter sensitivity analysis, recognition accuracy analysis. and comparison with other methods, is taken to verify the superiority of the proposed method.