Improved Genetic Algorithm Based Feature Selection Strategy Based Five Layered Artificial Neural Network Classifier (Iga – Flann)

Data classification is one of the investment research areas in the field of data mining. Machine learning algorithms such as naive bayes, neural network, and support vector machine are most regularly used for performing the classification task. Supervised learning is one of its kinds where the datasets consist of class labels and the machine learning classifier are trained first using that. It is to be noted that feature selection plays a vital role in developing the classification accuracy of the supervised machine learning classifiers. This research work aims in proposing an improved genetic algorithm based feature selection planning based five layered artificial neural network classifier. Around 20 datasets are collect from the UCI repository. Implementations are carried out using MATLAB tool. Performance metrics such as prediction efficiency and time taken for prediction are taken into account to conduct the performance evaluation of the expected classifier. Simulation results portrays that the proposed IGA-FLANN classifier outperforms the existing classifiers.