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This chapter reviews prevalent data preprocessing and feature selection methodologies employed in financial fraud detection research, addressing both class imbalance issues and the ‘curse of dimensionality.‘ Empirical research necessitates comprehensive consideration of data sampling methods, feature selection techniques, and machine learning algorithm selection to balance the multifaceted nature of financial fraud determinants, diversity of feature selection approaches, and scarcity of fraudulent instances.

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Resampling Techniques and Feature Selection

  • 章节

  • 发表日期：

    2025年

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  • Xiyuan Ma,
  • Desheng Wu

摘要

This chapter reviews prevalent data preprocessing and feature selection methodologies employed in financial fraud detection research, addressing both class imbalance issues and the ‘curse of dimensionality.‘ Empirical research necessitates comprehensive consideration of data sampling methods, feature selection techniques, and machine learning algorithm selection to balance the multifaceted nature of financial fraud determinants, diversity of feature selection approaches, and scarcity of fraudulent instances.