CAJ | 학술논문

针对已有轨迹异常检测算法无法有效处理轨迹多因素特征的问题，提出一种轨迹多因素异常检测集成算法T RODEM。采用集成分析技术，利用一种新颖的数据为中心的集成框架对轨迹数据多因素特征进行集成；对轨迹多因素特征分别进行检测，为每种特征检测的结果赋予一个用于集成的异常分值；利用组合函数对这些分值进行集成，得到一个最终异常分值，以此分值进行最终异常检测。在集成过程中，采用带有权值的累积方法克服可能存在的集成结果对某种因素过分依赖的缺点。实验结果表明了T RODEM算法的有效性和鲁棒性。
침대이유궤적이상검측산법무법유효처리궤적다인소특정적문제，제출일충궤적다인소이상검측집성산법T RODEM。채용집성분석기술，이용일충신영적수거위중심적집성광가대궤적수거다인소특정진행집성；대궤적다인소특정분별진행검측，위매충특정검측적결과부여일개용우집성적이상분치；이용조합함수대저사분치진행집성，득도일개최종이상분치，이차분치진행최종이상검측。재집성과정중，채용대유권치적루적방법극복가능존재적집성결과대모충인소과분의뢰적결점。실험결과표명료T RODEM산법적유효성화로봉성。
Most existing trajectory outlier detection algorithms fail to deal with multi‐factors of trajectory effectively .Aiming at the problem ,a trajectory outlier detection ensemble algorithm based on multi‐factors (TRODEM ) was proposed .The ensemble learning idea was adopted and a novel data‐centered ensemble framework was taken into the process of multi‐factors ensemble . Each factor was detected and then an outlier score was assigned to each of them .In the process of outlier ensemble ,a novel cu‐mulative sum method with weighed values was used as combine function to combine the scores .Experimental results show that T RODEM is robust and effective .