Improving statistical learning methods via features selection without replacement sampling and random projection

TL;DR

This paper introduces a novel machine learning approach combining feature selection without replacement sampling and random projection to enhance classification accuracy in high-dimensional microarray cancer datasets, effectively reducing overfitting.

Contribution

It presents a new integrated method using FSWOR and projection techniques, along with statistical gene selection, to improve microarray data classification accuracy.

Findings

Achieved 96% classification accuracy, outperforming existing methods by 9.09%.

Reduced feature space from 54,675 to 20,890 genes using Kendall test.

Demonstrated effectiveness in high-dimensional gene expression analysis.

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, resulting in overfitting. This study makes three different key contributions: 1) we propose a machine learning-based approach integrating the Feature Selection Without Re-placement (FSWOR) technique and a projection method to improve classification accuracy. 2) We apply the Kendall statistical test to identify the most significant genes from the brain cancer mi-croarray dataset (GSE50161), reducing the feature space from 54,675 to 20,890 genes.3) we apply machine learning models using k-fold cross validation techniques in which our model incorpo-rates ensemble classifiers with LDA projection and Na\"ive Bayes, achieving a test score of 96%, outperforming existing methods by 9.09%. The results demonstrate the effectiveness of our ap-proach in high-dimensional gene expression analysis, improving classification accuracy while mitigating overfitting. This study contributes to cancer biomarker discovery, offering a robust computational method for analyzing microarray data.