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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/2507.04251 |
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| _version_ | 1866918085035819008 |
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| author | Adhikari, Aayush Bhatta, Sandesh Jangwan, Harendra S. Mishra, Amit Nisa, Khair Ul Zamani, Abu Taha Sapkota, Aaron Muduli, Debendra Parveen, Nikhat |
| author_facet | Adhikari, Aayush Bhatta, Sandesh Jangwan, Harendra S. Mishra, Amit Nisa, Khair Ul Zamani, Abu Taha Sapkota, Aaron Muduli, Debendra Parveen, Nikhat |
| contents | High dimensionality in datasets produced by microarray technology presents a challenge for
Machine Learning (ML) algorithms, particularly in terms of dimensionality reduction and
handling imbalanced sample sizes. To mitigate the explained problems, we have proposedhybrid
ensemble feature selection techniques with majority voting classifier for micro array classi f
ication. Here we have considered both filter and wrapper-based feature selection techniques
including Mutual Information (MI), Chi-Square, Variance Threshold (VT), Least Absolute
Shrinkage and Selection Operator (LASSO), Analysis of Variance (ANOVA), and Recursive
Feature Elimination (RFE), followed by Particle Swarm Optimization (PSO) for selecting the
optimal features. This Artificial Intelligence (AI) approach leverages a Majority Voting Classifier
that combines multiple machine learning models, such as Logistic Regression (LR), Random
Forest (RF), and Extreme Gradient Boosting (XGBoost), to enhance overall performance and
accuracy. By leveraging the strengths of each model, the ensemble approach aims to provide
more reliable and effective diagnostic predictions. The efficacy of the proposed model has
been tested in both local and cloud environments. In the cloud environment, three virtual
machines virtual Central Processing Unit (vCPU) with size 8,16 and 64 bits, have been used
to demonstrate the model performance. From the experiment it has been observed that, virtual
Central Processing Unit (vCPU)-64 bits provides better classification accuracies of 95.89%,
97.50%, 99.13%, 99.58%, 99.11%, and 94.60% with six microarray datasets, Mixed Lineage
Leukemia (MLL), Leukemia, Small Round Blue Cell Tumors (SRBCT), Lymphoma, Ovarian,
andLung,respectively, validating the effectiveness of the proposed modelin bothlocalandcloud
environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04251 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ATwo-Stage Ensemble Feature Selection and Particle Swarm Optimization Approach for Micro-Array Data Classification in Distributed Computing Environments Adhikari, Aayush Bhatta, Sandesh Jangwan, Harendra S. Mishra, Amit Nisa, Khair Ul Zamani, Abu Taha Sapkota, Aaron Muduli, Debendra Parveen, Nikhat Machine Learning High dimensionality in datasets produced by microarray technology presents a challenge for Machine Learning (ML) algorithms, particularly in terms of dimensionality reduction and handling imbalanced sample sizes. To mitigate the explained problems, we have proposedhybrid ensemble feature selection techniques with majority voting classifier for micro array classi f ication. Here we have considered both filter and wrapper-based feature selection techniques including Mutual Information (MI), Chi-Square, Variance Threshold (VT), Least Absolute Shrinkage and Selection Operator (LASSO), Analysis of Variance (ANOVA), and Recursive Feature Elimination (RFE), followed by Particle Swarm Optimization (PSO) for selecting the optimal features. This Artificial Intelligence (AI) approach leverages a Majority Voting Classifier that combines multiple machine learning models, such as Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), to enhance overall performance and accuracy. By leveraging the strengths of each model, the ensemble approach aims to provide more reliable and effective diagnostic predictions. The efficacy of the proposed model has been tested in both local and cloud environments. In the cloud environment, three virtual machines virtual Central Processing Unit (vCPU) with size 8,16 and 64 bits, have been used to demonstrate the model performance. From the experiment it has been observed that, virtual Central Processing Unit (vCPU)-64 bits provides better classification accuracies of 95.89%, 97.50%, 99.13%, 99.58%, 99.11%, and 94.60% with six microarray datasets, Mixed Lineage Leukemia (MLL), Leukemia, Small Round Blue Cell Tumors (SRBCT), Lymphoma, Ovarian, andLung,respectively, validating the effectiveness of the proposed modelin bothlocalandcloud environments. |
| title | ATwo-Stage Ensemble Feature Selection and Particle Swarm Optimization Approach for Micro-Array Data Classification in Distributed Computing Environments |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.04251 |