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Machine Learning Metrics for Fraud Detection by gayanku.research is a document available to read on EtoBox.

What is Machine Learning Metrics for Fraud Detection about?

This study investigates the behavior of machine learning classifier evaluation metrics in the context of massively imbalanced and noisy credit card fraud detection data. It develops a theoretical model to account for human annotation errors and conducts empirical experiments to identify effective evaluation metrics, concluding that a combined F1 score and g-mean are the most suitable for this specific application. The findings highlight the challenges of accurately detecting fraud in highly imbalanced datas

Author

gayanku.research

Language

EN