Paper Info
Title
Deep rule-based classifier with human-level performance and characteristics.
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.
Year
DOI
Venue
2018
10.1016/j.ins.2018.06.048
Information Sciences
Keywords
Field
DocType
Fuzzy rule based classifiers,Deep learning,Non-parametric,Non-iterative,Self-evolving structure
Parallelizable manifold,Rule based classifier,Fuzzy logic,Image processing,Parametric statistics,Artificial intelligence,Contextual image classification,Classifier (linguistics),Machine learning,Mathematics
Journal
Volume
ISSN
Citations 
463
0020-0255
12
PageRank 
References 
Authors
0.55
33
2
Name
Order
Citations
PageRank
Plamen Angelov195467.44
Xiaowei Gu29910.96