| Autor: |
Chi, Haixiao, Lu, Yiwei, Liao, Beishui, Xu, Liaosa, Liu, Yaqi |
| Předmět: |
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| Zdroj: |
IEEE Intelligent Systems; Mar/Apr2021, Vol. 36 Issue 2, p52-63, 12p |
| Abstrakt: |
Since the existing machine-learning-based approaches for fraud detection are incapable of providing explanations, we propose a fraud detection method based on quantitative argumentation, which is intrinsically interpretable. First, we construct an argumentative tree by combining human-level knowledge and the knowledge learned from data. Second, we extend the existing quantitative argumentation debates (QuAD) frameworks by adding correlation strength between arguments and exploit the particle swarm optimization algorithm (PSO) to identify the correlation strength between arguments. Third, the performance of the new method is investigated by an empirical study, using the data from Ant Financial, the Alibaba Group's financial services provider. The results show that the new method has better performance than the existing DF-QuAD algorithm and is competitive with other machine learning methods, including Xgboost, ANN, SVM, and LR. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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