Enhanced data categorization using sigmoid-optimized support vector machines

doi:10.1007/s10586-026-06386-1

Enhanced data categorization using sigmoid-optimized support vector machines

2026-07-24

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AI

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Idriss Dagal *

W

Wulfran Fendzi Mbasso

A

Ambe Harrison

DOI：10.1007/s10586-026-06386-1

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Support Vector Machines are widely used for classification tasks due to their robustness in handling complex data distributions. However, traditional kernel functions often struggle with nonlinear boundaries in real-world datasets. This study pioneers an approach that enhances Support Vector Machine performance by integrating diverse sigmoid-based functions, including the Richards, Weibull, Michaelis-Menten, Double Boltzmann, and Swish models. These functions improve data separability by offering adaptive decision boundaries that align more naturally with clustered or overlapping classes. The proposed Sigmoid-Optimized Support Vector Machine is evaluated on various datasets, demonstrating up to 91.5% improvement in classification accuracy, reduced misclassification rates, and enhanced generalization capabilities compared to conventional Support Vector Machine kernels. Experimental results indicate that sigmoid-based fits improve feature scaling, optimize hyperplane placement, and enhance adaptability in high-dimensional spaces. This novel framework efficiently manages computational complexity, enabling more accurate and robust data categorization, which is especially beneficial for applications ranging from biomedical analysis to energy forecasting.

Keywords:

Support vector machine

Sigmoid functions

Richards model

Weibull distribution

Michaelis-Menten kinetics

Boltzmann function

Enhanced data categorization using sigmoid-optimized support vector machines

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