Classification models and hybrid feature selection method to improve crop performance

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.