MBabCoNN --- A Multiclass Version of a Constructive Neural Network Algorithm Based on Linear Separability and Convex Hull

Authors:
João Roberto Bertini, Jr.;Maria Carmo Nicoletti
Affiliations:
Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, Brazil;Department of Computer Science, Federal University of São Carlos, São Carlos, Brazil
Venue:
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Year:
2008

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Abstract

The essential characteristic of constructive neural network (CoNN) algorithms is the incremental construction of the neural network architecture along with the training process. The BabCoNN (Barycentric-based constructive neural network) algorithm is a new neural network constructive algorithm suitable for two-class problems that relies on the BCP (Barycentric Correction Procedure) for training its individual TLU (Threshold Logic Unit). Motivated by the good results obtained with the two-class BabCoNN, this paper proposes its extension to multiclass domains as a new CoNN algorithm named MBabCoNN. Besides describing the main concepts involved in the MBabCoNN proposal, the paper also presents a comparative analysis of its performance versus the multiclass versions of five well known constructive algorithms, in four knowledge domains as an empirical evidence of the MBabCoNN suitability and efficiency for multiclass classification tasks.