Classification is a core supervised learning task in data analysis, and six classical classifier families (k-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, Logistic Regression, and Naïve Bayes) remain widely used in practice and underpin many subsequent variants. Although both single-family and multi-classifier surveys exist, there is still a gap for a method-centered study that, within a coherent framework, combines algorithmic representations for training and prediction, methodological characteristics, an explicit methodological comparison of the foundational variants within each family, and method-oriented advances published between 2020 and 2025. The survey is organized around a fixed set of performance-related perspectives, including accuracy, hyperparameter tuning, scalability, class imbalance, behavior in high-dimensional settings, decision-boundary complexity, interpretability, computational efficiency, and multiclass handling. It highlights strengths, weaknesses, and trade-offs across the six families and their variants, helping researchers and practitioners select or extend classification approaches. It also outlines future research directions arising from the limitations across the examined methods.

KSII Transactions on Internet and Information Systems TIIS Paper Details

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