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.

翻译：多类别分类是机器学习中一项基础且具有挑战性的任务。现有的多类别分类技术可分为：(i) 分解为二值分类、(ii) 从二值分类扩展以及 (iii) 层次分类。将多类别分类分解为一组可通过二值分类器高效求解的二值分类问题，称为类别二值化，这是多类别分类中一种流行技术。神经进化是一种通用且强大的技术，用于演化神经网络的结构和权重，已成功应用于二值分类。本文我们将类别二值化技术应用于神经进化算法——增强拓扑的神经进化（NEAT），该算法用于生成多类别分类的神经网络。我们提出一种新方法，利用纠错输出编码（ECOC）来设计针对多类别分类的神经进化中的类别二值化策略。在三个著名数据集（Digit、Satellite 和 Ecoli）上，我们将ECOC策略与一对一和一对多的类别二值化策略进行了比较。我们从多类别分类退化、准确性、进化效率和鲁棒性四个方面分析其性能。结果表明，采用ECOC的NEAT在低方差下具有高准确性，尤其在灵活的二值分类器数量和强鲁棒性方面展现出显著优势。