Neural Learning from Unbalanced Data

This paper describes the result of our study on neural learning to solve the classification problems in which data is unbalanced and noisy. We conducted the study on three different neural network architectures, multilayered Back Propagation, Radial Basis Function, and Fuzzy ARTMAP using three different training methods, duplicating minority class examples, Snowball technique and multidimensional Gaussian modeling of data noise. Three major issues are addressed: neural learning from unbalanced data examples, neural learning from noisy data, and making intentional biased decisions. We argue that by properly generated extra training data examples around the noise densities, we can train a neural network that has a stronger capability of generalization and better control of the classification error of the trained neural network. In particular, we focus on problems that require a neural network to make favorable classification to a particular class such as classifying normal(pass)/abnormal...

• Type: Journal
• Year: 2004
• Where: APIN
• Authors: Yi Lu Murphey, Hong Guo, Lee A. Feldkamp
• Keywords: APIN 2004, Data Examples, Neural Network, Neural Network Architectures