Information fusion and attribute reduction for multi-source incomplete mixed data via conditional information entropy and D-S evidence theory

Abstract

Multi-source incomplete mixed data abound in real life, like medical data, biological data, remote sensing data, military data, etc. However, some of these sources are of less importance than others, and some are essentially worthless. Therefore, their information fusion and attribute reduction face many challenges. This paper studies information fusion for multi-source incomplete mixed data via conditional information entropy (CIE) and considers its application to attribute reduction based on D-S evidence theory. First of all, a new distance function is defined to measure the difference between nominal attribute values with missing information, and the neighborhood rough set model is used to establish the granularity structure of multi-source incomplete mixed data. Then, a source selection method is given via CIE, which is used to fuse multi-source incomplete mixed data into single-source incomplete mixed data. Based on the maximization of CIE, this method allows worthy and reliable information sources to be chosen. Next, the connection between neighborhood rough set model and D-S evidence theory is established. Moreover, two attribute reduction algorithms for the fused incomplete mixed data are proposed based on the belief and plausibility. Finally, experiments are done to verify the effectiveness of the proposed fusion and reduction algorithms. The results of experiment and statistical test on 12 datasets show that the proposed algorithms exceed other advanced algorithms in classification performance.