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RuDi: Explaining Behavior Sequence Models by Automatic Statistics Generation and Rule Distillation. (arXiv:2208.07211v2 [cs.LG] UPDATED)
Aug. 17, 2022, 1:11 a.m. | Yao Zhang, Yun Xiong, Yiheng Sun, Caihua Shan, Tian Lu, Hui Song, Yangyong Zhu
cs.LG updates on arXiv.org arxiv.org
Risk scoring systems have been widely deployed in many applications, which
assign risk scores to users according to their behavior sequences. Though many
deep learning methods with sophisticated designs have achieved promising
results, the black-box nature hinders their applications due to fairness,
explainability, and compliance consideration. Rule-based systems are considered
reliable in these sensitive scenarios. However, building a rule system is
labor-intensive. Experts need to find informative statistics from user behavior
sequences, design rules based on statistics and assign weights …
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