Multi-class pattern classification using neural networks

Authors:
Guobin Ou;Yi Lu Murphey
Affiliations:
Department of Electrical and Computer Engineering, The University of Michigan-Dearborn, Dearborn, MI 48128-1491, USA;Department of Electrical and Computer Engineering, The University of Michigan-Dearborn, Dearborn, MI 48128-1491, USA
Venue:
Pattern Recognition
Year:
2007

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Abstract

Multi-class pattern classification has many applications including text document classification, speech recognition, object recognition, etc. Multi-class pattern classification using neural networks is not a trivial extension from two-class neural networks. This paper presents a comprehensive and competitive study in multi-class neural learning with focuses on issues including neural network architecture, encoding schemes, training methodology and training time complexity. Our study includes multi-class pattern classification using either a system of multiple neural networks or a single neural network, and modeling pattern classes using one-against-all, one-against-one, one-against-higher-order, and P-against-Q. We also discuss implementations of these approaches and analyze training time complexity associated with each approach. We evaluate six different neural network system architectures for multi-class pattern classification along the dimensions of imbalanced data, large number of pattern classes, large vs. small training data through experiments conducted on well-known benchmark data.