An assessment method for the impact of missing data in the rough set-based decision fusion

Incomplete information systems with missing or unknown data may affect the quality of data-driven decision fusion directly. The impact of missing data and how much missing data is acceptable for a reliable decision-making become more important in the era of big data. This paper recommends the rough set theory for the decision fusion of incomplete information systems and proposes a new approach to evaluate the impact of missing data. According to the connection degree tolerance relation, an improved metric called a-classification quality of approximation is defined to measure the quality of decision fusion with various identical degrees (IDs). Then, the link between the volume of missing data and the quality of decision fusion is established. Furthermore, the relaxed connection degree tolerance relation is modified to reveal the impact of missing data in the classification, which makes the influence of changes in the volume of missing data become assessable. Thus, the assessment method of missing data is established. The experimental results have shown that the quantitative evaluation of missing data in an existing information system can be made by the proposed method and the volume of acceptable missing data according to a determined quality is possible to be predicted in future applications.