Improving statistical learning methods via features selection without replacement sampling and random projection
This work addresses overfitting in cancer biomarker discovery from gene expression data, offering a robust computational method for researchers in bioinformatics and oncology, though it appears incremental as it builds on existing techniques.
The study tackled the 'small n, large p' problem in high-dimensional microarray data for cancer classification by integrating feature selection without replacement and projection methods, achieving a test score of 96% and outperforming existing methods by 9.09%.
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 Naïve 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.