  Multiclass classification is a fundamental and challenging task in machine
learning. The existing techniques of multiclass classification can be
categorized as (i) decomposition into binary (ii) extension from binary and
(iii) hierarchical classification. Decomposing multiclass classification into a
set of binary classifications that can be efficiently solved by using binary
classifiers, called class binarization, which is a popular technique for
multiclass classification. Neuroevolution, a general and powerful technique for
evolving the structure and weights of neural networks, has been successfully
applied to binary classification. In this paper, we apply class binarization
techniques to a neuroevolution algorithm, NeuroEvolution of Augmenting
Topologies (NEAT), that is used to generate neural networks for multiclass
classification. We propose a new method that applies Error-Correcting Output
Codes (ECOC) to design the class binarization strategies on the neuroevolution
for multiclass classification. The ECOC strategies are compared with the class
binarization strategies of One-vs-One and One-vs-All on three well-known
datasets Digit, Satellite, and Ecoli. We analyse their performance from four
aspects of multiclass classification degradation, accuracy, evolutionary
efficiency, and robustness. The results show that the NEAT with ECOC performs
high accuracy with low variance. Specifically, it shows significant benefits in
a flexible number of binary classifiers and strong robustness.

Machine Learning;Neural and Evolutionary Computing