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Highly interpretable hierarchical deep rule-based classifier

Gu, Xiaowei, Angelov, Plamen P. (2020) Highly interpretable hierarchical deep rule-based classifier. Applied Soft Computing, 92 . Article Number 106310. ISSN 1568-4946. (doi:10.1016/j.asoc.2020.106310) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90183)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1016/j.asoc.2020.106310

Abstract

Pioneering the traditional fuzzy rule-based (FRB) systems, deep rule-based (DRB) classifiers are able to offer both human-level performance and transparent system structure on image classification problems by integrating zero-order fuzzy rule base with a multi-layer image-processing architecture that is typical for deep learning. Nonetheless, it is frequently observed that the inner structure of DRB can become over sophisticated and not interpretable for humans when applied to large-scale, complex problems. To tackle the issue, one feasible solution is to construct a tree structural classification model by aggregating the possibly huge number of prototypes identified from data into a much smaller number of more descriptive and highly abstract ones. Therefore, in this paper, we present a novel hierarchical deep rule-based (H-DRB) approach that is capable of summarizing the less descriptive raw prototypes into highly generalized ones and self-arranging them into a hierarchical prototype-based structure according to their descriptive abilities. By doing so, H-DRB can offer high-level performance and, most importantly, full transparency and human-interpretability on various problems including large-scale ones. The proposed concept and generical principles are verified through numerical experiments based on a wide variety of popular benchmark image sets. Numerical results demonstrate that the promise of H-DRB.

Item Type: Article
DOI/Identification number: 10.1016/j.asoc.2020.106310
Uncontrolled keywords: Deep rule-based; Hierarchical; Prototype-based; Self-organizing
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 13 Sep 2021 12:32 UTC
Last Modified: 14 Sep 2021 08:54 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90183 (The current URI for this page, for reference purposes)
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