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Multiclass Pattern Recognition Extension for the New C-Mantec Constructive Neural Network Algorithm
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DOI：10.1007/s12559-010-9051-6.png)
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The new C-Mantec algorithm constructs compact neural network architectures for classsification problems, incorporating new features like competition between neurons and a built-in filtering stage of noisy examples. It was originally designed for tackling two class problems and in this work the extension of the algorithm to multi-class problems is analyzed. Three different approaches are investigated for the extension of the algorithm to multi-category pattern classification tasks: One-Against-All (OAA), One-Against-One (OAO), and P-against-Q (PAQ). A set of different sizes benchmark problems is used in order to analyze the prediction accuracy of the three multiclass implemented schemes and to compare the results to those obtained using other three standard classification algorithms.
Keyword:
Supervised learning
Neural networks
Multiclass pattern recognition
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