Optimization for Best Feature Selection on Microarray Gene Expression Data

Abstract

Feature selection serves as a crucial technique in data analysis, eliminating unnecessary elements from a dataset to enhance computational efficiency and improve the accuracy of machine learning models. This study introduces a novel method called Rotate Left and Complement (RLC) for feature selection, employing T statistics to identify informative genes. The RLC algorithm, based on the top m informative genes, presents a promising solution to refine feature sets. The accuracy of categorization is evaluated using the KNN method across three diverse datasets, demonstrating the effectiveness of the proposed approach in optimizing feature selection and contributing to the advancement of machine learning methodologies.