# Augmented Regression Models using Neurochaos Learning

arXiv: https://arxiv.org/abs/2505.12967 · HTML: https://mlsanity.com/paper/2505.12967/

## Key facts

- Cited by 2 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 19 May 2025.

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

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [A Novel Chaos Theory Inspired Neuronal Architecture](https://mlsanity.com/paper/1905.12601/) (2019)

## Built on by (most cited first, 2 of 2)

- [Self-Training the Neurochaos Learning Algorithm](https://mlsanity.com/paper/2601.01146/) (2026)
- [Linked Data Classification using Neurochaos Learning](https://mlsanity.com/paper/2602.16204/) (2026)

## Abstract (arXiv)

This study presents novel Augmented Regression Models using Neurochaos Learning (NL), where Tracemean features derived from the Neurochaos Learning framework are integrated with traditional regression algorithms : Linear Regression, Ridge Regression, Lasso Regression, and Support Vector Regression (SVR). Our approach was evaluated using ten diverse real-life datasets and a synthetically generated dataset of the form y = mx + c + ε. Results show that incorporating the Tracemean feature (mean of the chaotic neural traces of the neurons in the NL architecture) significantly enhances regression performance, particularly in Augmented Lasso Regression and Augmented SVR, where six out of ten real-life datasets exhibited improved predictive accuracy. Among the models, Augmented Chaotic Ridge Regression achieved the highest average performance boost (11.35 %). Additionally, experiments on the simulated dataset demonstrated that the Mean Squared Error (MSE) of the augmented models consistently decreased and converged towards the Minimum Mean Squared Error (MMSE) as the sample size increased. This work demonstrates the potential of chaos-inspired features in regression tasks, offering a pathway to more accurate and computationally efficient prediction models.