Multiclass Pattern Recognition Extension for the New C-Mantec Constructive Neural Network Algorithm

• Published in: Cognitive computation Vol. 2; no. 4; pp. 285 - 290
• Main Authors: Subirats, José L., Jerez, José M., Gómez, Iván, Franco, Leonardo
• Format: Journal Article
• Language: English
• Published: New York Springer-Verlag 01-12-2010; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Biomedical and Life Sciences; Biomedicine; Classification; Competition; Computation by Abstract Devices; Computational Biology/Bioinformatics; Neural networks; Neurons; Neurosciences; Pattern classification; Pattern recognition; Multiclass pattern recognition; Supervised learning; Neural networks
• ISSN: 1866-9956; 1866-9964
• DOI: 10.1007/s12559-010-9051-6

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 multiclass 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 multi-class implemented schemes and to compare the results to those obtained using other three standard classification algorithms.