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Classification Models and Hybrid Feature Selection Method to Improve Crop Performance

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

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