A novel FLANN with a hybrid PSO and GA based Gradient Descent Learning for classification

Abstract

In this paper, it is an attempt to design a PSO & GA based FLANN model (PSO-GA-FLANN) for classification with a hybrid Gradient Descent Learning (GDL). The PSO, GA and the gradient descent search are used iteratively to adjust the parameters of FLANN until the error is less than the required value. Accuracy and convergence of PSO-GA-FLANN is investigated and compared with FLANN, GA-based FLANN and PSO-based FLANN. These models have been implemented and results are statistically analyzed using ANOVA test in order to get significant result. To obtain generalized performance, the proposed method has been tested under 5-fold cross validation.