CAJ | 학술논문

离群值检测的目的在于检测出不符合期望行为的数据，因而适合应用于如欺诈交易检测等多个领域。针对目前众多的通用离群值检测方法，本文侧重从机器学习的角度出发，分别从基于无监督、监督和半监督技术分析与比较三种不同销售欺诈交易检测模型，使用决策精确／回溯精确度和累积回溯精确度对其不同实验结果进行分析比较。
리군치검측적목적재우검측출불부합기망행위적수거，인이괄합응용우여기사교역검측등다개영역。침대목전음다적통용리군치검측방법，본문측중종궤기학습적각도출발，분별종기우무감독、감독화반감독기술분석여비교삼충불동소수기사교역검측모형，사용결책정학／회소정학도화루적회소정학도대기불동실험결과진행분석비교。
Outlier detection aims to detect those data that significantly deviate from the expected behavior, and thus is widely applied in fraudulent transaction detection, etc.In view of many algorithms for outlier detection, this paper, starting from machine learning, makes an analysis and comparison on three different fraudulent sale transaction models based respectively on unsupervised, supervised and semi-supervised technologies, and it analyzes and compares their experiment results by precision/recall(PR) and cumulative recall( CR) .