Data Analysis Approaches of Interval-Valued Fuzzy Soft Sets Under Incomplete Information

Interval-valued fuzzy soft set theory is a new and developing mathematical tool, which figures out a creative way aiming at dealing with uncertain and fuzzy data. Studies on decision making approaches and parameter reduction based on the complete interval-valued fuzzy soft sets became very active. However, we have to face up to a mount of incomplete data in real applications of interval-valued fuzzy soft sets. In this paper, we propose data analysis approaches of interval-valued fuzzy soft sets under incomplete information, which involves ignoring incomplete data when the percentage of missing entries is higher than the threshold and a filling approach for incomplete information while the percentage of missing entries is lower than the threshold. A suitable and practical case study demonstrates the implementation and validation of the proposed analysis approaches. The experimental results show that the overall accuracy estimation of the filling approach is up to 96.11%.