KNNCC: An algorithm for k-nearest neighbor clique clustering

Qu, Chao; Yuan, Ruifen; Wei, Xianglin

doi:10.1109/icmlc.2013.6890883

Cited by 4 publications

(1 citation statement)

References 5 publications

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“…The main idea of KNN was that a sample's category is decided by its k most similar samples. The sample falls into a category which contains the largest number of its k most similar samples (Qu, Yuan, & Wei, ). KNN classifier allows for an intuitive display of the prediction results (Paul et al, ).…”

Section: Methodsmentioning

confidence: 99%

Study on pesticide residues classification of lettuce leaves based on polarization spectroscopy

Zhou

Sun

et al. 2018

J Food Process Engineering

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In order to effectively implement the rapid and nondestructive testing of pesticide residues in lettuce leaves, polarization spectral detection technology was used in this article. There were 90 pieces of lettuce leaves from five different groups, as well as a total of 450 samples of lettuce were used to collect polarization spectral information. CARS, IRIV, and SPA were used to obtain optimal wavelengths. BP neural network, KNN, and SVM were used to establish classification models. The results showed that the significance of these parameters from primary factor to secondary factor were incident zenith angle A, detector polarization angle D, detecting zenith angle B, and sample stage azimuth E, respectively. Besides, optimal level of A, B, D, and E were 60°, 45°, 30°, and 270°, respectively. Furthermore, the best classification model different kinds of pesticide residues in lettuce leaves was CARS‐SVM model, with calibration identification rate of 100%, prediction identification rate of 97.78%. It confirms that polarization spectral detection technology is a feasible and effective method for classifying different pesticide residues in lettuce leaves. Practical applications It is of great significance to understand the effects of pesticide residues on the biological structure and to reveal new biological functions and mechanisms of action. In order to effectively implement the rapid and nondestructive testing of pesticide residues in lettuce leaves, polarization spectral detection technology was used in this article. In addition, the order and superior level of polarization spectral factor were obtained through the calculation of the range using orthogonal test. It confirms that the polarization spectra is a feasible and effective method for discriminating different pesticide residues in lettuce leaves.

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Section: Methodsmentioning

confidence: 99%