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Deep rule-based classifier with human-level performance and characteristics

Angelov, Plamen P., Gu, Xiaowei (2018) Deep rule-based classifier with human-level performance and characteristics. Information Sciences, 463-64 . pp. 196-213. ISSN 0020-0255. (doi:10.1016/j.ins.2018.06.048) (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:90113)

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.ins.2018.06.048

Abstract

In this paper, a new type of multilayer rule-based classifier is proposed and applied to image classification problems. The proposed approach is entirely data-driven and fully automatic. It is generic and can be applied to various classification and prediction problems, but in this paper we focus on image processing, in particular. The core of the classifier is a fully interpretable, understandable, self-organized set of IF…THEN… fuzzy rules based on the prototypes autonomously identified by using a one-pass type training process. The classifier can self-evolve and be updated continuously without a full retraining. Due to the prototype-based nature, it is non-parametric; its training process is non-iterative, highly parallelizable and computationally efficient. At the same time, the proposed approach is able to achieve very high classification accuracy on various benchmark datasets surpassing most of the published methods, be comparable with the human abilities. In addition, it can start classification from the first image of each class in the same way as humans do, which makes the proposed classifier suitable for real-time applications. Numerical examples of benchmark image processing demonstrate the merits of the proposed approach.

Item Type: Article
DOI/Identification number: 10.1016/j.ins.2018.06.048
Uncontrolled keywords: Fuzzy rule based classifiers: Deep learning; Non-parametric; Non-iterative; Self-evolving structure
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: 09 Sep 2021 15:05 UTC
Last Modified: 10 Sep 2021 10:43 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90113 (The current URI for this page, for reference purposes)
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