Using Imbalanced Triangle Synthetic Data for Machine Learning Anomaly Detection  

作　　者：Menghua Luo Ke Wang Zhiping Cai Anfeng Liu Yangyang Li Chak Fong Cheang 

机构地区：[1]College of Computer,National University of Defense Technology,Changsha,410073,China [2]Normal College of Jishou University,Jishou,416000,China [3]School of Information Science and Engineering,Central South University,Changsha,410083,China [4]Innovation Center,China Academy of Electronics and Information Technology,Beijing,100041,China [5]Faculty of Information Technology,Macao University of Science and Technology,519020,Macao

出　　处：《Computers, Materials & Continua》2019年第1期15-26,共12页计算机、材料和连续体（英文）

基　　金：This research was financially supported by the National Natural Science Foundation of China(Grant No.61379145);the Joint Funds of CETC(Grant No.20166141B020101).

摘　　要：The extreme imbalanced data problem is the core issue in anomaly detection.The amount of abnormal data is so small that we cannot get adequate information to analyze it.The mainstream methods focus on taking fully advantages of the normal data,of which the discrimination method is that the data not belonging to normal data distribution is the anomaly.From the view of data science,we concentrate on the abnormal data and generate artificial abnormal samples by machine learning method.In this kind of technologies,Synthetic Minority Over-sampling Technique and its improved algorithms are representative milestones,which generate synthetic examples randomly in selected line segments.In our work,we break the limitation of line segment and propose an Imbalanced Triangle Synthetic Data method.In theory,our method covers a wider range.In experiment with real world data,our method performs better than the SMOTE and its meliorations.

关 键 词：ANOMALY detection imbalanced DATA SYNTHETIC DATA machine learning 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]