Class Binarization to NeuroEvolution for Multiclass Classification

TL;DR

This paper introduces a novel approach combining class binarization with neuroevolution, specifically using ECOC strategies with NEAT, to improve multiclass classification accuracy, robustness, and efficiency.

Contribution

It proposes integrating Error-Correcting Output Codes with NEAT for multiclass classification, demonstrating improved performance and robustness over traditional binarization methods.

Findings

ECOC strategies with NEAT outperform One-vs-One and One-vs-All in accuracy.

The method shows high robustness and low variance across datasets.

It offers flexible binary classifier configurations for multiclass problems.

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.