Classification Techniques in Supervised Learning

Abdelrahim Aqqad

Chapter Chapter 14 in Fraud Analytics in Action, 2026, pp 335-369 from Springer

Abstract: Abstract This chapter provides a comprehensive examination of classification techniques in supervised learning, with a focused application to fraud data analytics. The discussion opens by distinguishing binary from multiclass classification and surveying the factors—data characteristics, computational constraints, and the trade-off between accuracy and interpretability—that guide algorithm selection. Logistic regression is presented as a transparent, probability-based baseline for binary problems, while support vector machines are introduced as margin-maximizing classifiers capable of handling non-linear separations through kernel functions, accompanied by guidance on tuning the regularization parameter C and the kernel-specific gamma. Decision trees are then explored for their interpretability and ability to handle mixed data types, followed by random forests as an ensemble extension that mitigates overfitting through bootstrap aggregation and majority voting. K-nearest neighbors is presented as a non-parametric, distance-based method, illustrated through a worked example using Euclidean distance over transaction features. The chapter further covers Naïve Bayes for probabilistic classification, gradient boosting variants including XGBoost, LightGBM, and CatBoost, and artificial neural networks for capturing complex non-linear patterns. The chapter closes with practical considerations of data shuffling and partitioning into training, validation, and testing sets to support generalization against evolving fraud patterns.

Date: 2026
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DOI: 10.1007/978-3-032-16023-2_14

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