Ohrid trout(Salamo letnica)is an endemic species of fish found in Lake Ohrid in the Former Yugoslav Republic of Macedonia(FYROM).The growth of Ohrid trout was examined in a controlled environment for a certain period,...Ohrid trout(Salamo letnica)is an endemic species of fish found in Lake Ohrid in the Former Yugoslav Republic of Macedonia(FYROM).The growth of Ohrid trout was examined in a controlled environment for a certain period,thereafter released into the lake to grow their natural population.The external features of the fish were measured regularly during the cultivation period in the laboratory to monitor their growth.The data mining methods-based computational model can be used for fast,accurate,reliable,automatic,and improved growth monitoring procedures and classification of Ohrid trout.With this motivation,a combined approach of principal component analysis(PCA)and support vectormachine(SVM)has been implemented for the visual discrimination and quantitative classification of Ohrid trout of the experimental and natural breeding and their growth stages.The PCA results in better discrimination of breeding categories of Ohrid trout at different development phases while the maximum classification accuracy of 98.33% was achieved using the combination of PCA and SVM.The classification performance of the combination of PCA and SVM has been compared to combinations of PCA and other classification methods(multilayer perceptron,naive Bayes,randomcommittee,decision stump,random forest,and random tree).Besides,the classification accuracy of multilayer perceptron using the original features has been studied.展开更多
基金supported by the National Natural Science Foundation of China (Grants No.51563022,21761018)the Program for Excellent Team of Scientific Research in Lanzhou Jiaotong University (Grant No.201706)~~
基金supported by the National Natural Science Foundation of China (Grant No.21968032)the Fundamental Research Funds of Central Universities-Innovation Team Cultivation Project (Grant No.31920190012)+1 种基金the Northwest Minzu University′s Double First-class and Characteristic Development Guide Special Funds-Chemistry Key Disciplines in Gansu Province (Grant No.11080316)the Teaching Quality and Reform Engineering Project of Gansu University (Grants No.2019GSSYJXSFZX01, 2019GSJXCGPY-16)
基金supported by the startup foundation for introducing talent of NUIST,Nanjing,China(Project No.2243141701103).
文摘Ohrid trout(Salamo letnica)is an endemic species of fish found in Lake Ohrid in the Former Yugoslav Republic of Macedonia(FYROM).The growth of Ohrid trout was examined in a controlled environment for a certain period,thereafter released into the lake to grow their natural population.The external features of the fish were measured regularly during the cultivation period in the laboratory to monitor their growth.The data mining methods-based computational model can be used for fast,accurate,reliable,automatic,and improved growth monitoring procedures and classification of Ohrid trout.With this motivation,a combined approach of principal component analysis(PCA)and support vectormachine(SVM)has been implemented for the visual discrimination and quantitative classification of Ohrid trout of the experimental and natural breeding and their growth stages.The PCA results in better discrimination of breeding categories of Ohrid trout at different development phases while the maximum classification accuracy of 98.33% was achieved using the combination of PCA and SVM.The classification performance of the combination of PCA and SVM has been compared to combinations of PCA and other classification methods(multilayer perceptron,naive Bayes,randomcommittee,decision stump,random forest,and random tree).Besides,the classification accuracy of multilayer perceptron using the original features has been studied.
