Self-Training the Neurochaos Learning Algorithm

TL;DR

This paper proposes a semi-supervised learning architecture combining Neurochaos Learning with Self-Training to improve classification accuracy on limited and imbalanced datasets, demonstrating significant performance gains.

Contribution

It introduces a novel hybrid SSL model that leverages chaos-based feature extraction and pseudo-labelling, outperforming standalone methods on benchmark datasets.

Findings

Achieved up to 188.66% performance improvement on Iris dataset.

Consistently outperformed standalone Self-Training models.

Enhanced generalisation and resilience in low-data scenarios.

Abstract

In numerous practical applications, acquiring substantial quantities of labelled data is challenging and expensive, but unlabelled data is readily accessible. Conventional supervised learning methods frequently underperform in scenarios characterised by little labelled data or imbalanced datasets. This study introduces a hybrid semi-supervised learning (SSL) architecture that integrates Neurochaos Learning (NL) with a threshold-based Self-Training (ST) method to overcome this constraint. The NL architecture converts input characteristics into chaos-based ring-rate representations that encapsulate nonlinear relationships within the data, whereas ST progressively enlarges the labelled set utilising high-confidence pseudo-labelled samples. The model's performance is assessed using ten benchmark datasets and five machine learning classifiers, with 85% of the training data considered unlabelled and just 15% utilised as labelled data. The proposed Self-Training Neurochaos Learning (NL+ST) architecture consistently attains superior performance gain relative to standalone ST models, especially on limited, nonlinear and imbalanced datasets like Iris (188.66%), Wine (158.58%) and Glass Identification (110.48%). The results indicate that using chaos-based feature extraction with SSL improves generalisation, resilience, and classification accuracy in low-data contexts.