A novel approach to information fusion in multi-source datasets: A granular computing viewpoint

The advent of Big Data has seen both the sources and volumes of data increase rapidly. A multi-source information system can be used to represent information drawn from multiple sources. However, some of these sources are of less importance than others, and some are essentially worthless. Selecting the most valuable sources and efficiently fusing information are therefore core issues in the field of data science. To investigate this matter, we first propose internal-confidence and external-confidence degrees to estimate the reliability of each information source within a multi-source information system. A source selection principle is then constructed, allowing worthy and reliable information sources to be chosen. Furthermore, a new information fusion method is constructed by transforming the original information of each object into a triangular fuzzy information granule, and some uncertainty measures of this fusion process are studied. Finally, to interpret and comprehend the proposed theories and algorithm, extensive experiments are performed on six datasets to verify that our approach can deal with practical issues. The results indicate that the proposed triangular fuzzy granule fusion approach is efficient and effective for information fusion in multi-source datasets. (C) 2016 Elsevier Inc. All rights reserved.