Impact of class noise on performance of hyperspectral band selection based on neighborhood rough set theory

Liu, Yao; Xiaoda, Cao; Meng, Xiao‐Li; Wu, Tao; Yan, Xiao; Luo, Qinghua

doi:10.1016/j.chemolab.2019.03.003

Cited by 6 publications

(1 citation statement)

References 37 publications

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“…However, when RS theory is used for continuous data processing, it needs to be discretized, which causes the loss of original data feature attributes. Therefore, the concept of neighborhood is introduced into RS theory to form a neighborhood rough set (NRS) model to solve the process of discretization of numeric characteristic variables in the data set [28]. The specific description is as follows:…”

Section: Spectral Feature Selection Algorithms 231 Neighborhood Rough...mentioning

confidence: 99%

Near-Infrared Spectroscopy Coupled with a Neighborhood Rough Set Algorithm for Identifying the Storage Status of Paddy

Yang,

Zhou,

et al. 2023

Applied Sciences

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Rapid and non-destructive identification of the suitable storage status of paddy during storage is crucial for controlling the quality of stored grains, which can provide high-quality raw grains for rice processing. Near-infrared (NIR) spectroscopy combined with neighborhood rough set (NRS) and multiple classification methods were used to identify the different storage statuses of paddy. The NIR data were collected in the range of 1000–1800 nm, and three storage statuses from suitable storage to severely unsuitable storage were divided using the measured fatty acid value of paddy. The spectral features were selected using NRS, successive projection algorithm and variable combination population analysis methods. Random forest (RF), extreme learning machine, and soft independent modeling of class analogy classifiers coupled with spectral features were used to establish classification models to distinguish the different storage statuses of paddy. The comparison results indicated that the optimal wavelengths selected by NRS combined with the RF classifier to construct the NRS-RF series models led to satisfactory identification results, with high correct classification rates of 96.31% and 93.68% in the calibration and test sets, respectively; the indicators of sensitivity and specificity ranged from 0.93 to 0.99. Therefore, the combination of NIR technology with NRS and RF algorithms for identifying the storage status of paddy was feasible, as this would be more helpful for rapidly evaluating the changes of stored paddy quality. The proposed method from this study is expected to provide support for the development of non-destructive equipment for the accurate detection of the quality of stored paddy.

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Section: Spectral Feature Selection Algorithms 231 Neighborhood Rough...mentioning

confidence: 99%

Near-Infrared Spectroscopy Coupled with a Neighborhood Rough Set Algorithm for Identifying the Storage Status of Paddy

Yang,

Zhou,

et al. 2023

Applied Sciences

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Discrimination of Tomato Maturity Using Hyperspectral Imaging Combined with Graph-Based Semi-supervised Method Considering Class Probability Information

et al. 2021

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Quantitative Detection of Myoglobin Content in Tan Mutton During Cold Storage by Near-infrared Hyperspectral Imaging

et al. 2022

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Impact of class noise on performance of hyperspectral band selection based on neighborhood rough set theory

Cited by 6 publications

References 37 publications

Near-Infrared Spectroscopy Coupled with a Neighborhood Rough Set Algorithm for Identifying the Storage Status of Paddy

Near-Infrared Spectroscopy Coupled with a Neighborhood Rough Set Algorithm for Identifying the Storage Status of Paddy

Discrimination of Tomato Maturity Using Hyperspectral Imaging Combined with Graph-Based Semi-supervised Method Considering Class Probability Information

Quantitative Detection of Myoglobin Content in Tan Mutton During Cold Storage by Near-infrared Hyperspectral Imaging

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