Information granule-based classifier: A development of granular imputation of missing data

Granular Computing (GrC) is a human-centric way to discover the fundamental structure of data sets. The resulting information granules can be efficiently exploited to organize knowledge and reveal data descriptions, which can play a pivotal role in the classification problems. Furthermore, information granules are abstract collections of data entities and exhibit flexibility and tolerance when it comes to the representation of incomplete data. However, most of the existing methods focused on the data imputation and classification separately. They also require better interpretability. The crux of this study is to develop a novel information granule-based classification method for incomplete data and a way of representing missing entities and regarding them as information granules in a unified framework. The first aspect focuses on revealing the structural backbone of multiple labeled subspaces of data by fuzzy clustering of missing values. It emerges a classifier with interpretable IF-THEN rules by the refinement of fuzzy prototypes in a supervised mode to capture the critical relationship of the multi-class incomplete data. The second aspect concerns the construction of some information granules to impute and represent missing values according to the refined prototypes and classification findings. The experimental studies involved synthetic and publicly available datasets in quantifying the advantages of the classification and representation abilities of the proposed methods on incomplete data. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

Granular Computing is a human-centric approach to discovering the fundamental structure of data sets, with information granules being used to organize knowledge and reveal data descriptions in classification problems. The focus of the study is on developing a novel information granule-based classification method for incomplete data, representing missing entities as information granules in a unified framework. Experimental studies demonstrated the advantages of the proposed methods on incomplete data classification and representation.