FS-GKM: A clustering-driven feature selection framework for enhanced supervised learning performance

2026-01-09T12:05:07+00:00

Feature selection is a fundamental process in machine learning that involves identifying and selecting the most informative features from a dataset to be used in model training. This step is crucial, as it can significantly enhance model performance, mitigate overfitting, reduce computational complexity, and improve the interpretability of the resulting models. By eliminating irrelevant or redundant features, feature selection facilitates the development of more efficient and accurate predictive models. Therefore, the development of new feature selection algorithms is of great importance. In this study, a novel feature selection algorithm—Feature Selection using Global k-Means (FS-GKM)—is proposed. In this study, a novel feature selection algorithm—Feature Selection using Global k-Means (FS-GKM)—is proposed. The method leverages the global k-means clustering algorithm to group features based on their similarity. Subsequently, the algorithm assesses the discriminative power of features by analyzing class density distributions within each cluster. This clustering-based evaluation enables the identification of features that contribute most significantly to class separability. To validate the effectiveness of the FS-GKM method, a comprehensive experimental analysis was conducted using 13 benchmark datasets. Each dataset was subjected to dimensionality reduction through 13 different feature selection techniques, and the resulting feature subsets were evaluated using four distinct classifiers. The proposed FS-GKM algorithm achieved superior performance in 40 out of 52 comparative cases, demonstrating its robustness and effectiveness across diverse scenarios.

Torse-forming vector fields and normalized null hypersurfaces

2026-01-29T07:56:37+00:00

The study focuses on normalized null hypersurfaces within semi-Riemannian manifolds that possess torse-forming vector fields. We demonstrate that these hypersurfaces exhibit a triple product structure. Additionally, we present obstruction results related to both topology and specific geometric criteria concerning the rigging and rigged vector field. We also provide a result on the splitting of the hypersurface, which is endowed with its rigged Riemannian structure. Furthermore, taking the potential vector field as the screen component of a torse-forming vector field, we investigate screen distribution with Ricci solitons.

International Journal of Maps in Mathematics

FS-GKM: A clustering-driven feature selection framework for enhanced supervised learning performance

Torse-forming vector fields and normalized null hypersurfaces