{"title":"pca预处理对KNN猪脸识别的影响研究","authors":"Hong-Ping Yan, Zhiwei Hu, Yiran Liu","doi":"10.35633/inmateh-70-08","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":44197,"journal":{"name":"INMATEH-Agricultural Engineering","volume":null,"pages":null},"PeriodicalIF":0.6000,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"STUDY ON THE INFLUENCE OF PCA PRE-TREATMENT ON PIG FACE IDENTIFICATION WITH KNN\",\"authors\":\"Hong-Ping Yan, Zhiwei Hu, Yiran Liu\",\"doi\":\"10.35633/inmateh-70-08\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":44197,\"journal\":{\"name\":\"INMATEH-Agricultural Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2023-08-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"INMATEH-Agricultural Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.35633/inmateh-70-08\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"AGRICULTURAL ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"INMATEH-Agricultural Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.35633/inmateh-70-08","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"AGRICULTURAL ENGINEERING","Score":null,"Total":0}
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.