A Framework for Selection of Neural Network Training Functions towards the Classification of Yeast Data - Datta, Shrayasi; Paulchoudhury, J (International Journal of Computer Sciences and Engineering, 2015)
Yeast is among the various important components for the formulation of medicine and various chemical products, so yeast data classification is an important bioinformatics task. Yeast data classification has been approached by various machine learning techniques for last few years. In this paper, an artificial neural network system with back propagation training algorithm is presented with different training functions for the classification of yeast dataset. Here an effort has been made to decide the suitable training functions of artificial neural network system for the classification of yeast protein. The training functions that have been used are, respectively, Batch Training, Batch Gradient Descent, Gradient Descent with momentum, Resilience back propagation, One-step secant back propagation, Scaled Conjugate back propagation, Conjugate Gradient back propagation with Polak-Riebre updates (CGP) and Conjugate Gradient back propagation with Fletcher-Reeves updates (CGF), BFGS and Levenberg-Marquardt training algorithm . The yeast dataset used for this purpose has been chosen and from UCI machine learning repository. The performance of the classification network has been tested by various performance measures like correctness of classification, mean square error, and regression analysis.