Feature Selection and Dimensionality Reduction Strategies for Optimizing Machine Learning Classification Models

Beatriz Almeida

Feature Selection and Dimensionality Reduction Strategies for Optimizing Machine Learning Classification Models

Author(s)	Beatriz Almeida
Country	Brazil
Abstract	Machine learning classification models frequently encounter computational and statistical difficulties when processing datasets containing large numbers of explanatory variables. High-dimensional datasets may include irrelevant attributes, redundant measurements, correlated features, and complex relationships that influence predictive performance. Although extensive feature representations can provide detailed information about individual observations, they may also increase computational requirements and introduce additional opportunities for model overfitting. Feature selection and dimensionality reduction provide methodological approaches for controlling these difficulties by identifying informative variables or constructing compact representations of the original data. The present paper develops a hypothetical comparative framework for examining feature selection and dimensionality reduction strategies used to optimize machine learning classification models. The proposed investigation considers filter-based selection, wrapper-based selection, embedded feature selection, Principal Component Analysis, and Linear Discriminant Analysis. A fictional dataset containing 3,600 observations and 600 numerical features is introduced to illustrate the experimental design. A linear Support Vector Machine serves as the common classification model for evaluating alternative feature representations. The proposed methodology incorporates data preprocessing, feature ranking, transformation procedures, model development, and independent performance evaluation. Classification accuracy and macro-averaged F1-score are considered primary performance measures. Two hypothetical analytical tables demonstrate possible differences in classification performance, retained dimensionality, and final classifier fitting time. The methodological discussion examines feature relevance, information preservation, selection stability, computational complexity, and evaluation bias. Particular attention is given to distinguishing feature selection from feature extraction and recognizing that reduced dimensionality does not necessarily guarantee improved classification. The paper concludes that feature optimization offers a valuable methodological direction for developing efficient predictive models when supported by appropriate validation procedures, reproducible analysis, and statistically reliable performance assessment.
Keywords	Feature Selection, Dimensionality Reduction, Machine Learning, Classification Optimization, Principal Component Analysis, Linear Discriminant Analysis, Recursive Feature Elimination, Embedded Learning, Predictive Accuracy, High-Dimensional Data.
Field	Engineering
Published In	Volume 1, Issue 4, September-October 2025
Published On	2025-10-14

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Feature Selection and Dimensionality Reduction Strategies for Optimizing Machine Learning Classification Models

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