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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.00053 |
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| _version_ | 1866912405312765952 |
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| author | khan, Sulaiman Ahmad, Muhammad Ullah, Fida Ibañez, Carlos Aguilar Rodriguez, José Eduardo Valdez |
| author_facet | khan, Sulaiman Ahmad, Muhammad Ullah, Fida Ibañez, Carlos Aguilar Rodriguez, José Eduardo Valdez |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00053 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving statistical learning methods via features selection without replacement sampling and random projection khan, Sulaiman Ahmad, Muhammad Ullah, Fida Ibañez, Carlos Aguilar Rodriguez, José Eduardo Valdez Quantitative Methods Artificial Intelligence Machine Learning Applications 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. |
| title | Improving statistical learning methods via features selection without replacement sampling and random projection |
| topic | Quantitative Methods Artificial Intelligence Machine Learning Applications |
| url | https://arxiv.org/abs/2506.00053 |