An information granule has to be translated into significant frameworks of granular computing to realize interpretability–accuracy tradeoff. These two objectives are in conflict and constitute an open problem. A new operational framework to form the evolving information granule (EIG) is developed in this paper, which ensures a compromise between interpretability and reasonable accuracy. The evolving information granule is initiated with the first information granule by translating the knowledge of the entire output domain. The initial information granule is considered an underfitting state with a high approximation error. Then, the EIG starts evolving in the information granule by partitioning the output domain and uses a dynamic constraint to maintain semantic interpretability in the output-contexts. The important criterion in the EIG is to determine the prominent distinction (output-context) in the output domain and realize the distinct information granule that depicts the semantics at the fuzzy partition level. The EIG tends to evolve toward the lower error region and realizes the effective rulebase by avoiding overfitting. The outcome on the synthetic and real-world data using the EIG shows the effectiveness of the proposed system, which outperforms state-of-the art methods. 
Information granularity model for evolving context-based fuzzy system