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| Spectral Open Set Recognition in Agriculture and Forestry Biological
Species Based on Fuzzy Rule Binary Classifier Combinations |
| HE Bao-xiong1, ZHAO Peng1*, LI Zhen-yu2 |
1. School of Computer Science and Software Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China
2. School of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
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Abstract Open set recognition requires that a classifier can not only identify testing samples from known classes but also reject those from unknown classes, which is rarely investigated in spectral analysis. In this article, we revise the conventional fuzzy rule multi-class classifier proposed by Ishibuchi for the closed-set scenario and apply it to open-set recognition. First, principal component analysis is used to reduce the spectral dimension of the original spectral curves, yielding 4- to 6-dimensional spectral feature vectors. Second, the fuzzy rule multi-class classifier proposed by Ishibuchi is simplified to a binary classifier, using a 1-vs-1 scheme to obtain a vote for each testing instance. Lastly, all votes from all binary classifiers are counted to determine the predicted class of the testing instance in the open-set scenario. If one known class gets the maximal vote and this vote is larger than a predetermined threshold τ, this testing instance is classified as this known class. Otherwise, it is rejected as an unknown class. The comparative experimental results across different groups of wood and mango spectral datasets indicate that our proposed scheme outperforms other state of the art open-set recognition schemes, such as the revised fuzzy rule multi-class classification based on generalized basic probability assignment, in the open-set scenario, with the best evaluation measures such as F-Score, Kappa coefficient, and overall recognition accuracy. Moreover, a dual-tailed McNemar's test is performed on the comparative experimental results from the mango spectral dataset to verify further that our proposed scheme is superior to other state of the art open-set recognition schemes.
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Received: 2025-06-01
Accepted: 2025-09-19
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Corresponding Authors:
ZHAO Peng
E-mail: bit_zhao@aliyun.com
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