Improving Statistical Learning Methods Via Features Selection without Replacement Sampling and Random Projection
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.
Fields of Study & Subjects
Face and Expression Recognition
Neural Networks and Applications
Publication details
- Journal / source
- Computación y Sistemas
- Publisher
- Instituto Politecnico Nacional/Centro de Investigacion en Computacion
- Volume
- 30
- Issue
- 1
- Publication date
- 31 March 2026
- Language
- EN