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Research Article Open access CC BY 4.0

Scalable Machine Learning Algorithms for Processing High-dimensional Data: A Case Study Approach

Oluwafunmilayo Ifeoluwa Somoye, Chimdi Walter Ndubuisi, Samuel Donatus, Felix Amakye

Asian Journal of Advanced Research and Reports · pp. 348–367 · Published 25 Jun 2025

10.9734/ajarr/2025/v19i61064

Abstract

The need for highly scalable machine learning algorithms is driven by the fast development of high-dimensional data in various industries, including cybersecurity and finance. Traditional models perform under heavy computational load and suffer reduced predictive performance when handling really massive data sets. This study method explored scalable ML approaches using a 100,000-sample, 5,000-feature dataset. Preprocessing involved normalization and missing value imputation followed by dimensionality reduction using Principal Component Analysis and Recursive Feature Elimination techniques subsequently. DNNs and other models like XGBoost were trained using TensorFlow on massively distributed frameworks pretty quickly. DNNs attained highest accuracy of 94.7% rather slowly after 180 minutes of training while XGBoost struck a nice balance between performance and efficiency with accuracy of 91.2% in just 72 minutes. RFE maintained accuracy with only a 1% drop, while PCA reduced computation time by about 40% at the expense of a negligible 2-3% accuracy reduction. An ANOVA F value of 38.52 at p less than 0.001 indicated significant variations in efficiency across the different models. Distributed XGBoost achieved around a 3.5× speedup hence showcasing great practical scalability in a moderately distributed context. While deep learning models are known for their great accuracy, tree-based models, like XGBoost, provide a better overall solution. Future research should focus on highly adaptive dimensionality reduction approaches and mainly hybrid models in order to substantially increase scalability without significantly sacrificing prediction accuracy.

High-dimensional data scalable machine learning dimensionality reduction computational efficiency model optimization

References (22)

  1. 1 Next-Generation Sequencing Technology: Current Trends and Advancements [DOI]
  2. 2 A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction [DOI]
  3. 3 High-Dimensional Data Analysis with Low-Dimensional Models [DOI]
  4. 4 Evaluating prediction model performance [DOI]
  5. 5 Review of Dimension Reduction Methods [DOI]
  6. 6 Application of Convolutional Neural Networks and Recurrent Neural Networks in Food Safety [DOI]
  7. 7 A hybrid machine learning model for intrusion detection in wireless sensor networks leveraging data balancing and dimensionality reduction [DOI]
  8. 8 Hybrid deep learning approach to improve classification of low-volume high-dimensional data [DOI]
  9. 9 Systematic Review on Missing Data Imputation Techniques with Machine Learning Algorithms for Healthcare [DOI]
  10. 10 An Ensemble of Random Forest Gradient Boosting Machine and Deep Learning Methods for Stock Price Prediction [DOI]
  11. 11 Review of filtering based feature selection for Botnet detection in the Internet of Things [DOI]
  12. 12 Scalable machine‐learning algorithms for big data analytics: a comprehensive review [DOI]
  13. 13 Principal Component Analysis and t-Distributed Stochastic Neighbor Embedding Analysis in the Study of Quantum Approximate Optimization Algorithm Entangled and Non-Entangled Mixing Operators [DOI]
  14. 14 Bidirectional encoder representations from transformers and deep learning model for analyzing smartphone-related tweets [DOI]
  15. 15 Reducing cross-validation variance through seed blocking in hyperparameter tuning [DOI]
  16. 16 Gradient-free training of recurrent neural networks using random perturbations [DOI]
  17. 17 Revolutionizing machine learning: Harnessing hardware accelerators for enhanced AI efficiency [DOI]
  18. 18 Advancing Material Stability Prediction: Leveraging Machine Learning and High-Dimensional Data for Improved Accuracy [DOI]
  19. 19 Performance Evaluation of Machine Learning and Deep Learning-Based Models for Predicting Remaining Capacity of Lithium-Ion Batteries [DOI]
  20. 20 A tied-weight autoencoder for the linear dimensionality reduction of sample data [DOI]
  21. 21 Review and Experimental Analysis on the Integration of Modern Tools for the Optimization of Data Center Performance [DOI]
  22. 22 Unified Machine Learning Techniques for High-Dimensional Survival Data Analysis [DOI]

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