Designing a Robust, Bounded, and Smooth Loss Function for Improved Supervised Learning

• URL: http://arxiv.org/abs/2602.06858v1
• Date: Fri, 06 Feb 2026 16:46:29 GMT
• Title: Designing a Robust, Bounded, and Smooth Loss Function for Improved Supervised Learning
• Authors: Soumi Mahato, Lineesh M. C,
• Abstract summary: We develop a robust, bounded, and smooth (RoBoS-NN) loss function to deal with high-dimensional and outlier-sensitive datasets.<n>We implement RoboS-NN loss in the framework of a neural network (NN) to forecast time series and present a new robust algorithm named $mathcalL_textRoBoS$-NN.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The loss function is crucial to machine learning, especially in supervised learning frameworks. It is a fundamental component that controls the behavior and general efficacy of learning algorithms. However, despite their widespread use, traditional loss functions have significant drawbacks when dealing with high-dimensional and outlier-sensitive datasets, which frequently results in reduced performance and slower convergence during training. In this work, we develop a robust, bounded, and smooth (RoBoS-NN) loss function to resolve the aforementioned hindrances. The generalization ability of the loss function has also been theoretically analyzed to rigorously justify its robustness. Moreover, we implement RoboS-NN loss in the framework of a neural network (NN) to forecast time series and present a new robust algorithm named $\mathcal{L}_{\text{RoBoS}}$-NN. To assess the potential of $\mathcal{L}_{\text{RoBoS}}$-NN, we conduct experiments on multiple real-world datasets. In addition, we infuse outliers into data sets to evaluate the performance of $\mathcal{L}_{\text{RoBoS}}$-NN in more challenging scenarios. Numerical results show that $\mathcal{L}_{\text{RoBoS}}$-NN outperforms the other benchmark models in terms of accuracy measures.

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