# Class Binarization to NeuroEvolution for Multiclass Classification

arXiv: https://arxiv.org/abs/2308.13876 · HTML: https://mlsanity.com/paper/2308.13876/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 31 references that are in the map.
- First posted on arXiv 26 Aug 2023.

## Builds on (most cited first, 3 of 3)

- [Metrics for Multi-Class Classification: an Overview](https://mlsanity.com/paper/2008.05756/) (2020)
- [Learning Directed Locomotion in Modular Robots with Evolvable Morphologies](https://mlsanity.com/paper/2001.07804/) (2020)
- [Evolving Parsimonious Networks by Mixing Activation Functions](https://mlsanity.com/paper/1703.07122/) (2017)

## Built on by (most cited first, 14 of 14)

- [Exploration of the Assessment for AVP Algorithm Training in Underground Parking Garages Simulation Scenario](https://mlsanity.com/paper/2311.08410/) (2023)
- [Deep learning for 3D Object Detection and Tracking in Autonomous Driving: A Brief Survey](https://mlsanity.com/paper/2311.06043/) (2023)
- [Dental Severity Assessment through Few-shot Learning and SBERT Fine-tuning](https://mlsanity.com/paper/2402.15755/) (2024)
- [Automatic parking planning control method based on improved A* algorithm](https://mlsanity.com/paper/2406.15429/) (2024)
- [ML-SceGen: A Multi-level Scenario Generation Framework](https://mlsanity.com/paper/2501.10782/) (2025)
- [Real-time pedestrian recognition on low computational resources](https://mlsanity.com/paper/2309.01353/) (2023)
- [User authentication system based on human exhaled breath physics](https://mlsanity.com/paper/2401.02447/) (2024)
- [Automated Parking Planning with Vision-Based BEV Approach](https://mlsanity.com/paper/2406.15430/) (2024)
- [Acceleration method for generating perception failure scenarios based on editing Markov process](https://mlsanity.com/paper/2407.00980/) (2024)
- [Deep Reinforcement Learning for Adverse Garage Scenario Generation](https://mlsanity.com/paper/2407.01333/) (2024)
- [Research on Reliable and Safe Occupancy Grid Prediction in Underground Parking Lots](https://mlsanity.com/paper/2407.02197/) (2024)
- [Development of Occupancy Prediction Algorithm for Underground Parking Lots](https://mlsanity.com/paper/2409.00923/) (2024)
- [Manboformer: Learning Gaussian Representations via Spatial-temporal Attention Mechanism](https://mlsanity.com/paper/2503.04863/) (2025)
- [Data Shift of Object Detection in Autonomous Driving](https://mlsanity.com/paper/2508.11868/) (2025)

## Abstract (arXiv)

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.