Applying WEKA towards Machine Learning With Genetic Algorithm and Back-propagation Neural Networks

Abstract

Machine learning aims of facilitating complex system data analysis, optimization, classification and prediction with the use of different mathematical and statistical algorithms. In this research, we are interested in establishing the process of estimating best optimal input parameters to train networks. Using WEKA, this paper implements a classifier with Back-propagation Neural Networks and Genetic Algorithm towards efficient data classification and optimization. The implemented classifier is capable of reading and analyzing a number of populations in giving datasets, and based on the identified population it estimates kinds of species in a population, hidden layers, momentum, accuracy, correct and incorrect instances.