A Comparative Study of Supervised Learning Algorithms for Real-Time Classification Tasks

Manasa S

doi:10.36948/ijfmr.2025.v07i03.49854

A Comparative Study of Supervised Learning Algorithms for Real-Time Classification Tasks

Author(s)	Ms. Manasa S
Country	India
Abstract	This study presents a comprehensive comparative analysis of supervised learning algorithms for real-time classification tasks across various domains such as healthcare, finance, and transportation. With the growing demand for accurate and efficient real-time systems, this research evaluates key algorithms—including decision trees, support vector machines, and neural networks—focusing on their predictive performance, speed, adaptability, and response to challenges like class imbalance and concept drift. Drawing from 150 peer-reviewed articles and empirical evaluations on twelve diverse datasets, the study assesses the impact of hyperparameter tuning using metrics such as accuracy, precision, recall, and area under the curve (AUC). Results underscore the significance of algorithm-context alignment and reveal that while certain algorithms excel in static environments, others adapt better to dynamic data streams. Moreover, the study emphasizes the need for multi-criteria evaluation frameworks and robust experimental testbeds to ensure practical applicability. Challenges such as evolving data distributions and imbalanced classes are highlighted, prompting a call for more adaptive and optimized models. This research provides valuable insights for practitioners and researchers, guiding the selection and fine-tuning of supervised learning models for effective real-time classification.
Keywords	Supervised Learning, Real-Time Classification, Machine Learning Algorithms, Class Imbalance, Concept Drift, Hyperparameter Tuning, Performance Metrics, Ensemble Methods
Field	Computer Applications
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-06-30
DOI	https://doi.org/10.36948/ijfmr.2025.v07i03.49854
Short DOI	https://doi.org/g9r794

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A Comparative Study of Supervised Learning Algorithms for Real-Time Classification Tasks

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