论文标题
通过决策树调节解释可解释的离群值检测
Explainable outlier detection through decision tree conditioning
论文作者
论文摘要
这项工作描述了基于RuleQuest Research开发的GRITBOT软件的异常检测程序（称为“ Outliertree”），该程序可松散地基于RuleQuest Research开发的Gritbot软件，该软件通过评估和跟随监督的决策树对变量的监督决策树拆分，其分支1-D置信区间是为目标变量和潜在置信器构建的分支1D置信区间，并根据这些置信区间进行了标记。在这种逻辑下，可以通过考虑决策树分支条件以及落入同一分支的非外观分布统计的一般分布统计数据来产生人类可读的解释，以说明为什么可以将变量的给定值视为离群值。监督的拆分有助于确保生成的条件不是虚假的，而是与目标变量和具有逻辑断点相关的。
This work describes an outlier detection procedure (named "OutlierTree") loosely based on the GritBot software developed by RuleQuest research, which works by evaluating and following supervised decision tree splits on variables, in whose branches 1-d confidence intervals are constructed for the target variable and potential outliers flagged according to these confidence intervals. Under this logic, it's possible to produce human-readable explanations for why a given value of a variable in an observation can be considered as outlier, by considering the decision tree branch conditions along with general distribution statistics among the non-outlier observations that fell into the same branch, which can then be contrasted against the value which lies outside the CI. The supervised splits help to ensure that the generated conditions are not spurious, but rather related to the target variable and having logical breakpoints.
