Comparative Evaluation of Ensemble Learning Algorithms for High-Dimensional Data Classification

Farhan Azmi bin Abdullah

Comparative Evaluation of Ensemble Learning Algorithms for High-Dimensional Data Classification

Author(s)	Farhan Azmi bin Abdullah
Country	Malaysia
Abstract	High-dimensional data classification represents a significant area of investigation within machine learning because modern computational applications frequently involve datasets containing large numbers of explanatory variables. Such datasets may exhibit complex interactions, redundant attributes, correlated features, and limited observations relative to the dimensionality of the feature space. These characteristics can influence classification accuracy, computational efficiency, and the generalization capabilities of predictive algorithms. Ensemble learning provides a methodological approach to addressing selected classification difficulties by combining multiple predictive models through structured aggregation procedures. The present paper develops a hypothetical comparative framework for examining the classification characteristics of five ensemble learning algorithms: Bagging, Random Forest, Extremely Randomized Trees, AdaBoost, and Gradient Boosting. The proposed methodology considers a fictional binary classification dataset containing 3,000 observations and 2,400 numerical attributes. Feature selection, model training, hyperparameter optimization, and independent evaluation are incorporated into the proposed research design. Particular attention is given to predictive performance under increasing feature dimensionality and the methodological implications of irrelevant or redundant variables. Two illustrative analytical tables demonstrate how classification outcomes and feature-dimensionality effects could be reported. The first presents hypothetical classification results using accuracy and F1-score, while the second describes illustrative performance differences across selected feature subsets. These numerical values are manually constructed and do not represent executed simulations or experimentally verified findings. The methodological discussion examines the relationship among ensemble diversity, feature selection, model complexity, computational efficiency, and predictive reliability. The study identifies evaluation bias, excessive dimensionality, and class imbalance as important considerations when comparing learning algorithms. Overall, the proposed framework provides a structured foundation for future empirical investigations involving ensemble-based classification of high-dimensional datasets. Its conclusions concern methodological design rather than demonstrated algorithmic superiority.
Keywords	Ensemble Learning, High-Dimensional Data, Data Classification, Random Forest, Gradient Boosting, Bagging, AdaBoost, Feature Selection, Machine Learning, Predictive Accuracy.
Field	Engineering
Published In	Volume 1, Issue 3, July-August 2025
Published On	2025-07-02

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Comparative Evaluation of Ensemble Learning Algorithms for High-Dimensional Data Classification

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