Cross-dataset evaluation of support vector machines: A reproducible, calibration-aware baseline for tabular classification

Syafi'ah, Nurus, Jamhuri, Mohammad, Pranata, Farahnas Imaniyah, Kusumastuti, Ari, Juhari, Juhari, Pagalay, Usman and Khudzaifah, Muhammad (2025) Cross-dataset evaluation of support vector machines: A reproducible, calibration-aware baseline for tabular classification. Jurnal Riset Mahasiswa Matematika, 4 (6). pp. 323-336. ISSN 2808-1552; E-ISSN 2808-4926

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Abstract

Support Vector Machines (SVMs) remain competitive for small and medium-sized tabular classification problems, yet reported results on benchmark datasets vary widely due to inconsistent preprocessing, validation, and probability calibration. This paper presents a calibration-aware, cross-dataset benchmark that evaluates SVMs against classical baselines—Logistic Regression, Decision Tree, and Random Forest—under leakage-safe pipelines and statistically principled protocols. Using three representative binary datasets (Titanic survival, Pima Indians Diabetes, and UCI Heart Disease), we standardize imputation, encoding, scaling, and nested cross-validation to ensure comparability. Performance is assessed not only on discrimination metrics (accuracy, precision, recall, F1, PR--AUC) but also on probability reliability (Brier score, Expected Calibration Error) and threshold optimization. Results show that tuned RBF--SVMs consistently outperform Logistic Regression and Decision Trees, and perform comparably to Random Forests. Calibration (Platt scaling, isotonic regression) substantially reduces error and improves decision quality, while domain-specific features enhance Titanic prediction. By embedding all steps in a transparent, reproducible protocol and validating across multiple datasets, this study establishes a rigorous methodological baseline for SVMs in tabular binary classification, providing a reference point for future machine learning research.

Item Type:	Journal Article
Keywords:	tabular classification; support vector machine; probability calibration; cross-dataset benchmarking; small datasets
Subjects:	01 MATHEMATICAL SCIENCES > 0102 Applied Mathematics > 010201 Approximation Theory and Asymptotic Methods 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics 01 MATHEMATICAL SCIENCES > 0102 Applied Mathematics 01 MATHEMATICAL SCIENCES > 0104 Statistics
Divisions:	Faculty of Mathematics and Sciences > Department of Mathematics
Depositing User:	Juhari Juhari
Date Deposited:	10 Jul 2026 14:39

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