Class Binarization to NeuroEvolution for Multiclass Classification

• URL: http://arxiv.org/abs/2308.13876v1
• Date: Sat, 26 Aug 2023 13:26:13 GMT
• Title: Class Binarization to NeuroEvolution for Multiclass Classification
• Authors: Gongjin Lan, Zhenyu Gao, Lingyao Tong, Ting Liu
• Abstract summary: Multiclass classification is a fundamental and challenging task in machine learning. Decomposing multiclass classification into a set of binary classifications is called class binarization. We propose a new method that applies Error-Correcting Output Codes (ECOC) to design the class binarization strategies on the neuroevolution for multiclass classification.
• Score: 9.179400849826216
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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