NEURAL NETWORK BASED DEEP LEARNING AND ENSEMBLE TECHNIQUES FOR DATA CLASSIFICATION

Abstract

n this paper, four neural network architectures are proposed for data classification. The four neural networks are constructed based on deep learning and ensemble architectures. Supervised and unsupervised learning paradigms are adopted. Stack of Supervised Neural Network (SSNN), Stack of Unsupervised Neural Network (SUNN), Ensemble of Supervised Neural Network (ESNN), and Ensemble of Unsupervised Neural Network (ESNN) are the proposed neural network classifiers. Australian credit approval data set from the University of California, Irvin is used to evaluate the classifiers. Supervised neural networks have produced more accurate classification results than unsupervised networks. Stack architectures are comparatively better than ensemble architectures. It is found from this research that the combination of supervised learning method and stack architecture leads to better performance.