Paper Info
Title
Information granule-based classifier: A development of granular imputation of missing data
Abstract
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.
Year
DOI
Venue
2021
10.1016/j.knosys.2020.106737
Knowledge-Based Systems
Keywords
DocType
Volume
Granular computing,Classification model,Data imputation,Fuzzy clustering,Principle of justifiable granularity
Journal
214
ISSN
Citations 
PageRank 
0950-7051
1
0.35
References 
Authors
0
4
Name
Order
Citations
PageRank
Xingchen Hu194.52
W. Pedrycz2139661005.85
Keyu Wu310.35
Yinghua Shen41386.12