用混沌特征+自训练，小样本分类效果提升超100%。
Self-Training the Neurochaos Learning Algorithm
- 将输入转为混沌脉冲表示，捕捉非线性关系。
- 用高置信度伪标签逐步扩充训练集，15%标签即达高精度。
- 适合数据少、类别不平衡场景，如鸢尾花、红酒数据集。
在诸多实际应用中，获取大量标注数据困难且昂贵，而未标注数据易得。传统监督学习在标注数据少或数据分布不均时表现不佳。本文提出一种融合神经混沌学习（NL）与阈值自训练（ST）的混合半监督学习架构。NL将输入特征转换为基于混沌的脉冲率表示，以捕捉数据中的非线性关系；ST则通过高置信度伪标签逐步扩充标注集。模型在十个基准数据集上，使用五种分类器进行评估，其中85%训练数据为未标注，仅15%为标注。所提的NL+ST架构在多数情况下优于独立的ST模型，尤其在数据量少、非线性明显且不平衡的数据集上表现突出，如鸢尾花（提升188.66%）、葡萄酒（158.58%）、玻璃识别（110.48%）。结果表明，结合混沌特征提取与半监督学习可显著提升低数据场景下的泛化能力、鲁棒性与分类准确率。
原文摘要 · Abstract (English)
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.
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