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

作者：

Highlights：

• We proposed a novel information granule-based classifier to reveal the structural of the subspaces of data, which is easier to be interpreted.

• We considered incomplete data among classification modeling, so that this classifier can deal with the incomplete data straightforwardly.

• We present a refinement mechanism for the information granule-based classifier to optimize the prototypes of the classification rules.

• As a byproduct of the classification, the imputed information granules are distinguished from present data and have more tolerance to the imputation error.

摘要

•We proposed a novel information granule-based classifier to reveal the structural of the subspaces of data, which is easier to be interpreted.•We considered incomplete data among classification modeling, so that this classifier can deal with the incomplete data straightforwardly.•We present a refinement mechanism for the information granule-based classifier to optimize the prototypes of the classification rules.•As a byproduct of the classification, the imputed information granules are distinguished from present data and have more tolerance to the imputation error.

论文关键词：Granular computing,Classification model,Data imputation,Fuzzy clustering,Principle of justifiable granularity

论文评审过程：Received 16 September 2020, Revised 15 December 2020, Accepted 30 December 2020, Available online 2 January 2021, Version of Record 11 January 2021.

论文官网地址：https://doi.org/10.1016/j.knosys.2020.106737