Publication Type : Journal Article
Publisher : International Journal of Innovative Technology and Exploring Engineering (IJITEE)- Blue Eyes Intelligence Engineering & Sciences Publication(Scopus)
Source : International Journal of Innovative Technology and Exploring Engineering (IJITEE)- Blue Eyes Intelligence Engineering & Sciences Publication(Scopus), Volume-8, Issue-11S2,
Url : https://www.ijitee.org/wp-content/uploads/papers/v8i11S2/K105209811S219.pdf
Campus : Chennai
School : School of Engineering
Department : Computer Science
Year : 2019
Abstract : In this paper classification models and hybrid
feature selection methods are implanted on benchmark dataset
on the Mango and Maize. Particle Swarm Optimization–Support
Vector Machine (PSO-SVM) classification algorithm for the
selection of important features from the Mango and Maize
datasets to analysis and also compare with the novel
classification techniques. Various experiments conducted on these datasets, provide more generated rules and high selection of features using PSO-SVM algorithm and Fuzzy Decision Tree. The proposed method yield high accuracy output as compared to the existing methods with minimum Error Rate and Maximum Positive Rate
Cite this Research Publication : U.Muthaiah, S.Markkandeyan, Y.Seetha, “Classification Models and Hybrid Feature Selection Method to Improve Crop Performance” International Journal of Innovative Technology and Exploring Engineering (IJITEE)- Blue Eyes Intelligence Engineering & Sciences Publication(Scopus), Volume-8, Issue-11S2, 2019
