Incremental Self-learning Reasoning in Briefed Partially-ordered Case Base:A Tax Inspection Case
偏序约简框架下增量自学习推理：税务稽查的例子

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, under which algorithms of case retrieval, retain, adaptation in CBR circle are offered. This enables an incremental self-learning mechanism on target cases. Experimental result has shown advantages over linear reasoning mechanism. This reasoning machine can be widely embeded in tax inspection, credit card fraud detection, and financial auditing applications.