N B, Harikrishnan and Vats, Anuja and Nagaraj, Nithin and Pedersen, Marius (2025) Chaotic map based compression approach to classification. arXiv. pp. 1-8.
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Chaotic_ Map _based _Compression _Approach _to _Classification.pdf - Published Version Restricted to Registered users only Download (984kB) |
| Contribution | Name |
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| Abstract: | 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 Lüroth 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. |
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| Item Type: | Journal Paper |
| Subjects: | School of Natural and Engineering Sciences > Complex Systems |
| Divisions: | Schools > Natural Sciences and Engineering |
| Date Deposited: | 30 Jan 2026 10:17 |
| Last Modified: | 30 Jan 2026 10:17 |
| Official URL: | https://arxiv.org/abs/2502.12302 |
| Related URLs: | |
| Funders: | * |
| Projects: | * |
| DOI: | |
| URI: | http://eprints.nias.res.in/id/eprint/3075 |
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