Feature space transformation in the support vector method

Objectives . This study focuses on the development and investigation of a generalized nonlinear Support Vector Machine (SVM) method incorporating an adaptive transformation of the feature space. Its aim is to improve computational efficiency while maintaining high classification accuracy. The binary classification problem is used as a case study. The main objective of the research is to quantitatively evaluate the performance of the proposed approach when compared to classical SVM models using fixed kernel functions, and to analyze how the transformation parameters affect classification quality. Methods . The proposed approach involves a preliminary transformation of the input data using a learnable nonlinear mapping with a fixed structure. This mapping is implemented as a composition of elementary functions and is parameterized by a limited number of trainable weights which allows control over model complexity. A linear SVM with L2 regularization is applied after the transformation. The model is trained using conventional, unconstrained numerical optimization methods. The classification quality is evaluated using the Accuracy metric averaged over 10-fold cross-validation. The work also studies the behavior of the model with varying feature space dimensionality. In addition, computational complexity is analyzed in terms of the number of operations and inference time required on test datasets. Results . Numerical experiments demonstrate that the proposed model significantly reduces classification time when compared to a polynomial-kernel SVM, while maintaining a comparable level of accuracy. The runtime analysis confirms that the proposed approach scales much better than traditional kernel methods. At the same time, the structure of the model remains interpretable and can be further adapted to the specifics of the application domain. Conclusions . The method developed provides an efficient alternative to traditional kernel-based algorithms. Through the use of a parameterized transformation of the feature space, the method enables adaptability, interpretability, and scalability, making it promising for practical applications in machine learning tasks.