Abstract
Data incompleteness is a common problem in real-life datasets. This is caused by acquisition problems, sensor failures, human errors, and so on. Missing values and their subsequent imputation can significantly affect the performance of data-driven models and can also distort the interpretability of explainable artificial intelligence (XAI) models, such as fuzzy models. This paper presents a novel imputation algorithm based on granular computing. This method benefits from the local structure of the dataset, explored using the granular approach. The method elaborates a set of granules that are then used to impute missing values in the dataset. The method is evaluated on several datasets and compared with several state-of-the-art imputation methods, both directly and indirectly. The direct evaluation compares the imputed values with the original data. The indirect evaluation compares the performance of fuzzy models built with TSK and ANNBFIS neuro-fuzzy systems. This enables not only the evaluation of the quality of numerically imputed values but also their impact on the interpretability of the constructed fuzzy models. This paper is accompanied by numerical experiments. The implementation of the method is available in a public GitHub repository.