A Novel Dynamic Fusion Approach Using Information Entropy for Interval-Valued Ordered Datasets

Information fusion is capable of fusing and transforming information originated from multiple sources into an integrated representation. As an important representative of information, interval-valued ordered data aims at characterizing inaccurate and ambiguous information. However, existing methods are inappropriate when applied to the multi-source fusion for them. In addition, it is inevitable that in daily-life the sources and attributes of multi-source information systems may update at the same time. Hence there is a need to fuse data as efficiently as possible. Inspired by these deficiencies, we pay attention to the effective and efficient fusion of multi-source interval-valued ordered data in varieties of cases. First, the concepts of fuzzy dominance relation and dominance classes based on it are put forward between any two samples of interval-valued ordered information systems. Second, we define the fuzzy dominating and dominated conditional entropy. Then the fusion model is established and it is multi-source interval-valued ordered data oriented. Furthermore, it is true that there are numerous real-life applications related to concurrent change of both sources and attributes. Consequently, we design four incremental mechanisms and algorithms for fusing multi-source interval-valued data on the ground of the static condition. Eventually, a series of experiments is carried out on twelve datasets to verify that the proposed fusion approach outperforms other comparative methods on efficacy. Meanwhile, our incremental fusion algorithms are efficient compared with the static one for updating multi-source interval-valued data when sources and attributes are in the simultaneous variation.