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

Angelov, Plamen Parvanov and Gu, Xiaowei (2018) Deep rule-based classifier with human-level performance and characteristics. Information Sciences, 463-46. pp. 196-213. ISSN 0020-0255

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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-organised 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:
Journal Article
Journal or Publication Title:
Information Sciences
Additional Information:
This is the author’s version of a work that was accepted for publication in Information Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences, 463-464, 2018 DOI: 10.1016/j.ins.2018.06.048
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1706
Subjects:
ID Code:
126098
Deposited By:
Deposited On:
17 Jul 2018 11:26
Refereed?:
Yes
Published?:
Published
Last Modified:
27 Oct 2020 07:31