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

By Plamen Parvanov Angelov, Xiaowei Gu. 2018

63 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology

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Citations in the corpus

Actively Semi-Supervised Deep Rule-based Classifier Applied to Adverse Driving Scenarios (2019, methodology): “It is also able to self-evolve its structure and self-update its meta-parameters as newly observed training images arrive from the data stream, which makes the classifier applicable for real-time applications [9], [20].”