Improving Statistical Learning Methods Via Features Selection without Replacement Sampling and Random Projection
Year: 2026
Type: article
Abstract: Cancer is fundamentally a genetic disease characterized by genetic and epigenetic alterations that disrupt normal gene expression, leading to uncontrolled cell growth and metastasis. High-dimensional microarray datasets pose challenges for classification models due to the "small n, large p" problem, which can lead to overfitting. This study makes three different key contributions: 1) We propose a machine learning-based approach integrating the Feature Selection Without Replacement (FSWOR) technique and a projection method to improve classification accuracy.
Source: Computación y Sistemas
Authors: Sulaiman Khan, Muhammad Ahmad, Fida Ullah, Carlos Fernando Aguilar Ibañez, José Eduardo Valdez Rodriguez
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W4414888094
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