Open Access
ARTICLE
A Combined Approach of Principal Component Analysis and Support Vector Machine for Early Development Phase Modeling of Ohrid Trout (Salmo Letnica)
Sunil Kr. Jha1,*, Ivan Uzunov2, Xiaorui Zhang1
1 School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China
2 Faculty of Computer Science and Engineering, University of Information Science and Technology, Ohrid, 6000, Republic of Macedonia
* Corresponding Author: Sunil Kr. Jha. Email:
Computer Modeling in Engineering & Sciences 2021, 126(3), 991-1009. https://doi.org/10.32604/cmes.2021.011821
Received 30 May 2020; Accepted 04 December 2020; Issue published 19 February 2021
Abstract
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 vector machine (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, naïve Bayes, random committee, decision stump, random
forest, and random tree). Besides, the classification accuracy of multilayer perceptron using the original features
has been studied.
Keywords
Cite This Article
APA Style
Jha, S.K., Uzunov, I., Zhang, X. (2021). A combined approach of principal component analysis and support vector machine for early development phase modeling of ohrid trout (salmo letnica). Computer Modeling in Engineering & Sciences, 126(3), 991-1009. https://doi.org/10.32604/cmes.2021.011821
Vancouver Style
Jha SK, Uzunov I, Zhang X. A combined approach of principal component analysis and support vector machine for early development phase modeling of ohrid trout (salmo letnica). Comput Model Eng Sci. 2021;126(3):991-1009 https://doi.org/10.32604/cmes.2021.011821
IEEE Style
S.K. Jha, I. Uzunov, and X. Zhang "A Combined Approach of Principal Component Analysis and Support Vector Machine for Early Development Phase Modeling of Ohrid Trout (Salmo Letnica)," Comput. Model. Eng. Sci., vol. 126, no. 3, pp. 991-1009. 2021. https://doi.org/10.32604/cmes.2021.011821