Granular models as networks of associations of information granules: A development scheme via augmented principle of justifiable granularity

Tai Long Jing, Cong Wang, Witold Pedrycz, Zhi Wu Li, Giancarlo Succi, Meng Chu Zhou

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

This study proposes an approach to the construction of granular models directly based on information granules expressed both in input and output spaces. Associating these information granules, the constructed granular models come in the framework of three layers networks: input granules, an inference scheme and output granules. The proposed approach consists of two stages. First, an augmented principle of justifiable granularity is proposed and applied to construct information granules in an input space. This principle constructs information granules not only through establishing a sound balance between two criteria, i.e., coverage and specificity, but also by optimizing those information granules on the basis of their homogeneity assessed with respect to data localized in output space. At the second stage, we propose an inference scheme by analyzing a location of an input datum in relation with the already formed information granules in an input space. The computed relation can be quantified as membership grades, thus yielding aggregation results involving information granules in an output space. The performance of the proposed granular model is supported by the mechanisms of granular computing and the principle of justifiable granularity. Experimental studies concerning synthetic and publicly available data are performed and some comparative analysis involving rule-based models is given.

Original languageEnglish (US)
Article number108062
JournalApplied Soft Computing
Volume115
DOIs
StatePublished - Jan 2022

All Science Journal Classification (ASJC) codes

  • Software

Keywords

  • Augmented principle of justifiable granularity
  • Granular inference scheme
  • Granular network
  • Granular wrapper

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