| Abstract: |
The exponential growth of databases across various domains necessitates robust techniques for handling missing data to maintain data integrity and analytical accuracy. Traditional approaches often struggle with real-valued datasets due to inherent limitations in handling uncertainty and imprecision. Nearest Neighbourhood algorithms have proven beneficial in missing data imputation, offering effective solutions to address data gaps. In this paper, we propose a novel method for missing data imputation, termed Intuitionistic Fuzzy Rough-Nearest Neighbourhood Imputation (IFR-NNI), which extends the application of intuitionistic fuzzy rough sets to handle missing data scenarios. By integrating Intuitionistic Fuzzy Rough Sets into the nearest neighbor imputation framework, we aim to overcome the limitations of traditional methods, including information loss, challenges in managing uncertainty and vagueness, and instability in approximation outcomes. The proposed method is implemented on real-valued datasets, and non-parametric statistical analysis is performed to evaluate its performance. Our findings indicate that the IFR-NNI method demonstrates excellent performance in general, showcasing its effectiveness in addressing missing data scenarios and advancing the field of data imputation methodologies. |