An instance-oriented multi-source information fusion technique based on neighborhood granules

Abstract

With the rapid development of society and technology, human beings have increasingly diverse sources of data collection. Consequently, multi-source information fusion techniques, aiming to utilize various technologies to process, integrate and analyze data from different sources to obtain valuable information, have attracted significant attention. As a structured and hierarchical manner by processing and analyzing data via “granules”, granular computing has been extensively applied to multi-source information fusion. Given that different information sources may contain redundant instances and noise at different levels, it is crucial to select representative instances from multiple information sources based on granular computing. However, there exists little research on instance-oriented fusion based on granular computing. To fill this gap, we investigate the issue of instance-oriented fusion in multi-source neighborhood decision information systems in this paper. Specifically, considering both the distribution and decision information of the neighborhood of an instance, we firstly propose the concept of internal confidence to reflect the reliable degree of an instance in an information source. Secondly, the external confidence is presented to measure the reliable degrees of information sources by employing the overlap degree of the neighborhood granules in multiple information sources. Then, by combining the internal confidence and the external confidence, we put forward a confidence index for instances within an information source to select representative instances from multiple information sources. Furthermore, we present an instance-oriented multi-source information fusion algorithm based on neighborhood granules (IoMsIF). Finally, the performance of IoMsIF is assessed by numerical experiments. The experimental results show that IoMsIF achieves satisfactory performance.