HENOS: A Henon Map-Based Chaotic Oversampling Strategy for Imbalanced Data Classification

Abstract

The effectiveness of machine learning models is profoundly influenced by the quality and distribution of training data. However, real-world datasets are often highly imbalanced, where conventional classification algorithms tend to favor the majority class, resulting in poor recognition of minority instances. To address this challenge, we propose HENOS, a novel oversampling method that leverages the nonlinear dynamics of the Henon map to synthesize minority class samples in a more structurally diverse and boundary-aware manner. Unlike traditional interpolation-based techniques such as SMOTE, which often fail to capture complex data distributions, HENOS introduces a deterministic yet chaotic mechanism to generate synthetic instances that preserve local data characteristics while enhancing class separability. We conduct comprehensive experiments on 37 benchmark datasets, evaluating HENOS across three distinct classifiers AdaBoost, Naive Bayes, and Artificial Neural Networks (ANN) using Area Under the Curve (AUC) and F1-score metrics. The results, validated by the Friedman statistical test, demonstrate that HENOS consistently outperforms existing oversampling methods with statistically significant gains. These findings highlight the potential of chaos-theoretic principles in tackling data imbalance and open avenues for their integration into advanced learning systems, including deep architectures.