Multi-class pattern classification using neural networks

By Guobin Ou, Yi Lu Murphey. Pattern Recognition, 2007

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A review of clustering techniques and developments (2017, background): “There has been a tremendous amount of work in supervised classification and evidently has been reported in the literature widely [2-9].”
A review of modularization techniques in artificial neural networks (2019, background): “A compromise between the two previous schemes is P-Against-Q (PAQ) (Subirats et al., 2010), where each subproblem aims to differentiate P number of classes from Q number of classes.”
Multiclass Imbalance Problems: Analysis and Potential Solutions (2012, background): “Data points in each class are generated randomly from Gaussian distributions, where the mean and standard deviation of each attribute are random real values in [0, 10].”