计算机科学 ›› 2007, Vol. 34 ›› Issue (12): 197-200.

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偏序约简框架下增量自学习推理:税务稽查的例子

  

  • 出版日期:2018-11-16 发布日期:2018-11-16
  • 基金资助:
    省科技计划项目(2006811201004)、暨南大学博士启动基金(51104653).

  • Online:2018-11-16 Published:2018-11-16

摘要: 本文讨论欺诈防范领域中税务稽查的例子。在相关文献基础上分析了目前线性推理的不足,提出构造偏序约简范例集,给出了CBR循环过程中范例获取、记忆、扩容、推理等算法,由此实现范例推理机增量自学习机制。算法相比线性检索和记忆有着较高的性能和准确度,在税务稽核选案、信用卡欺诈、公司财务数据审计方面都可以有相当广阔的应用。

关键词: 范例推理 偏序 自学习

Abstract: Tax inspection is a special category in fraud detection area. Based on the investigation on related works, this paper discusses the weaknesses of current linear reasoning in case-based reasoning context. We propose a partially-ordered briefed case base, u

Key words: Case-based reasoning, Partial order, Self-learning

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