Open AccessAutomatic and objective gradation of 114 183 terrorist attacks using a machine learning approach
Author(s) -
Chi Wanle,
Du Yihong
Publication year - 2021
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.2020-0138
Subject(s) - gradation , terrorism , property (philosophy) , grading (engineering) , computer science , ranking (information retrieval) , computer security , risk analysis (engineering) , rank (graph theory) , engineering , artificial intelligence , civil engineering , mathematics , business , law , political science , philosophy , epistemology , combinatorics
Catastrophic events cause casualties, damage property, and lead to huge social impacts. To build common standards and facilitate international communications regarding disasters, the relevant authorities in social management rank them in subjectively imposed terms such as direct economic losses and loss of life. Terrorist attacks involving uncertain human factors, which are roughly graded based on the rule of property damage, are even more difficult to interpret and assess. In this paper, we collected 114 183 open‐source records of terrorist attacks and used a machine learning method to grade them synthetically in an automatic and objective way. No subjective claims or personal preferences were involved in the grading, and each derived common factor contains the comprehensive and rich information of many variables. Our work presents a new automatic ranking approach and is suitable for a broad range of gradation problems. Furthermore, we can use this model to grade all such attacks globally and visualize them to provide new insights.