# Chaotic Map based Compression Approach to Classification

arXiv: https://arxiv.org/abs/2502.12302 · HTML: https://mlsanity.com/paper/2502.12302/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 23 references that are in the map.
- First posted on arXiv 17 Feb 2025.

## Builds on (most cited first, 2 of 2)

- [The Description Length of Deep Learning Models](https://mlsanity.com/paper/1802.07044/) (2018)
- [Minimum Description Length Recurrent Neural Networks](https://mlsanity.com/paper/2111.00600/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Compression Based Classification Framework Using Symbolic Dynamics of Chaotic Maps](https://mlsanity.com/paper/2508.02330/) (2025)

## Abstract (arXiv)

Modern machine learning approaches often prioritize performance at the cost of increased complexity, computational demands, and reduced interpretability. This paper introduces a novel framework that challenges this trend by reinterpreting learning from an information-theoretic perspective, viewing it as a search for encoding schemes that capture intrinsic data structures through compact representations. Rather than following the conventional approach of fitting data to complex models, we propose a fundamentally different method that maps data to intervals of initial conditions in a dynamical system. Our GLS (Generalized L"uroth Series) coding compression classifier employs skew tent maps - a class of chaotic maps - both for encoding data into initial conditions and for subsequent recovery. The effectiveness of this simple framework is noteworthy, with performance closely approaching that of well-established machine learning methods. On the breast cancer dataset, our approach achieves 92.98% accuracy, comparable to Naive Bayes at 94.74%. While these results do not exceed state-of-the-art performance, the significance of our contribution lies not in outperforming existing methods but in demonstrating that a fundamentally simpler, more interpretable approach can achieve competitive results.