Augmented Regression Models using Neurochaos Learning

TL;DR

This paper introduces Augmented Regression Models that incorporate Neurochaos Learning features, significantly improving predictive accuracy across various datasets and demonstrating the effectiveness of chaos-inspired features in regression tasks.

Contribution

The paper presents a novel integration of Neurochaos Learning features with traditional regression algorithms, enhancing their performance in real-world and synthetic datasets.

Findings

Augmented models outperform traditional ones in most datasets.

Augmented Ridge Regression achieved an 11.35% performance boost.

MSE decreases and converges with increasing sample size.

Abstract

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 $y = mx + c + \epsilon$. 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.