Information fusion for multi-scale data: Survey and challenges

Abstract

Information fusion is a useful technique of combining and merging different information to form a more complete and accurate result. Traditional information fusion models mainly focus on the single-scale data in which each object has a unique value for any attribute. However, in practice, an object may take on different values under the same attribute, depending on the scale used to measure it. Information fusion of multi-scale data has become a hot topic in the field of intelligent computing. In the past decade, various models and algorithms of multi-scale information fusion (MIF) with rough set theory have been proposed. In this paper, a detailed and comprehensive review about the current research developments of MIF is carried out. First, the multi-scale decision system is introduced to perform the knowledge representation of multi-scale data. On the basis, the classical model of MIF, i.e., the Wu–Leung model, is presented. Second, some MIF models with different information granulation and information fusion strategies are summarized, respectively. Next, for optimal granularity selection, which is the key issue of MIF, existing information measurements interpreting consistency criteria are listed and analyzed, and the common strategies of scale fusion and attribute fusion in optimal granularity selection are summarized. Then, the local MIF and the applications of MIF are reviewed, respectively. Finally, the potential research directions and challenges of MIF are discussed.