A Novel Multi-Source Information Fusion Method Based on Dependency Interval

Abstract

With the rapid development of Big Data era, it is necessary to extract necessary information from a large amount of information. Single-source information systems are often affected by extreme values and outliers, so multi-source information systems are more common and data more reasonable, information fusion is a common method to deal with multi-source information system. Compared with single-valued data, interval-valued data can describe the uncertainty and random change of data more effectively. This article proposes a novel interval-valued multi-source information fusion method: A multi-source information fusion method based on dependency interval. This method needs to construct a dependency function, which takes into account the interval length and the number of data points in the interval, so as to make the obtained data more centralized and eliminate the influence of outliers and extreme values. Due to the unfixed boundary of the dependency interval, a median point within the interval is selected as a bridge to simplify the acquisition of the dependency interval. Furthermore, a multi-source information system fusion algorithm based on dependency intervals was proposed, and experiments were conducted on 9 UCI datasets to compare the classification accuracy and quality of the proposed algorithm with traditional information fusion methods. The experimental results show that this method is more effective than the maximum interval method, quartile interval method, and mean interval method, and the validity of the data has been proven through hypothesis testing.