pca预处理对KNN猪脸识别的影响研究

IF 0.6 Q4 AGRICULTURAL ENGINEERING INMATEH-Agricultural Engineering Pub Date : 2023-08-17 DOI:10.35633/inmateh-70-08
Hong-Ping Yan, Zhiwei Hu, Yiran Liu
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引用次数: 0

摘要

为了探索传统机器学习模型在猪的智能管理中的应用,本文研究了PCA预处理对KNN猪人脸识别的影响。采用检验方法,对10头不同品种的猪进行个体识别试验,分为单独采用KNN和采用PCA + KNN两种测试方案,分类器参数分别取3和5。优化后的方案运行效率显著提高,训练时间和测试时间分别减少到单纯KNN方案的4.8%和7%,但准确率有一定程度的降低。综合考虑这些因素,PCA预处理有利于个体猪对KNN的识别。为KNN分类器的移动终端和嵌入式应用提供实验支持。
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STUDY ON THE INFLUENCE OF PCA PRE-TREATMENT ON PIG FACE IDENTIFICATION WITH KNN
To explore the application of traditional machine learning model in the intelligent management of pigs, in this paper, the influence of the PCA pre-treatment on pig face identification with KNN is studied. With testing method, individual identification test was carried out on 10 different pigs in two testing schemes, in which one adopted KNN alone and the other adopted PCA + KNN, for which the classifier parameter was taken as 3 and 5, respectively. In the optimized scheme, the operating efficiency got significantly increased, also the training time and testing time were reduced to 4.8% and 7% of the original value in the KNN alone scheme, though the accuracy got lowered to a certain extent. With all these factors taken into consideration, PCA pre-treatment is beneficial to individual pig identification with KNN. It can provide experimental support for mobile terminals and embedded application of KNN classifiers.
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来源期刊
INMATEH-Agricultural Engineering
INMATEH-Agricultural Engineering AGRICULTURAL ENGINEERING-
CiteScore
1.30
自引率
57.10%
发文量
98
期刊最新文献
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