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Semi-supervised deep rule-based approach for image classification
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DOI：10.1016/j.asoc.2018.03.032.png)
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摘要
En 中文
In this paper, a semi-supervised learning approach based on a deep rule-based (DRB) classifier is introduced. With its unique prototype-based nature, the semi-supervised DRB (SSDRB) classifier is able to generate human interpretable IF...THEN...rules through the semi-supervised learning process in a self organising and highly transparent manner. It supports online learning on a sample-by-sample basis or on a chunk-by-chunk basis. It is also able to perform classification on out-of-sample images. Moreover, the SSDRB classifier can learn new classes from unlabelled images in an active way becoming dynamically self-evolving. Numerical examples based on large-scale benchmark image sets demonstrate the strong performance of the proposed SSDRB classifier as well as its distinctive features compared with the state-of-the-art approaches. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Semi-supervised learning
Deep rule-based (DRB) classifier
Prototype-based models
Fuzzy rules
Self-organising classifier
Transparency and interpretability
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期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
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