A Combined Approach of Principal Component Analysis and Support Vector Machine for Early Development Phase Modeling of Ohrid Trout(Salmo Letnica)

作     者：Sunil Kr.Jha Ivan Uzunov Xiaorui Zhang 

作者机构：School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjing210044China Faculty of Computer Science and EngineeringUniversity of Information Science and TechnologyOhrid6000Republic of Macedonia 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2021年第126卷第3期

页      面：991-1009页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the startup foundation for introducing talent of NUIST Nanjing China(Project No.2243141701103) 

主　　题：Salamo letnica growth phase modeling PCA SVM 

摘      要：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 *** external features of the fish were measured regularly during the cultivation period in the laboratory to monitor their *** data mining methods-based computational model can be used for fast,accurate,reliable,automatic,and improved growth monitoring procedures and classification of Ohrid *** 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 *** 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 *** 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.