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Enhanced data categorization using sigmoid-optimized support vector machines
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AI
DOI：10.1007/s10586-026-06386-1.png)
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Abstract
En 中文
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
Journal
C
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4.1
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5.1K
Citations:
7.5K
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