Two-parameter KNN algorithm and its application in recognition of brand rice
| Autor: | Mingjuan Song, Linna Li, Siyu Zhu, Chongnan He |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
Two parameter Computer science business.industry 0211 other engineering and technologies General Engineering Pattern recognition 02 engineering and technology k-nearest neighbors algorithm Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business 021101 geological & geomatics engineering |
| Zdroj: | Journal of Intelligent & Fuzzy Systems. 41:1837-1843 |
| ISSN: | 1875-8967 1064-1246 |
| DOI: | 10.3233/jifs-210584 |
| Popis: | In response to the frequent counterfeiting of Wuchang rice in the market, an effective method to identify brand rice is proposed. Taking the near-infrared spectroscopy data of a total of 373 grains of rice from the four origins (Wuchang, Shangzhi, Yanshou, and Fangzheng) as the observations, kernel principal component analysis(KPCA) was employed to reduce the dimensionality, and Fisher discriminant analysis(FDA) and k-nearest neighbor algorithm (KNN) were used to identify brand rice respectively. The effects of the two recognition methods are very good, and that of KNN is relatively better. Howerver the shortcomings of KNN are obvious. For instance, it has only one test dimension and its test of samples is not delicate enough. In order to further improve the recognition accuracy, fuzzy k-nearest neighbor set is defined and fuzzy probability theory is employed to get a new recognition method –Two-Parameter KNN discrimination method. Compared with KNN algorithm, this method increases the examination dimension. It not only examines the proportion of the number of samples in each pattern class in the k-nearest neighbor set, but also examines the degree of similarity between the center of each pattern class and the sample to be identified. Therefore, the recognition process is more delicate and the recognition accuracy is higher. In the identification of brand rice, the discriminant accuracy of Two-Parameter KNN algorithm is significantly higher than that of FDA and that of KNN algorithm. |
| Databáze: | OpenAIRE |
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