Event Predictability: A Uniform Form for IoT-Based Nondeterministic Social Systems

被引:1
作者
Duan, Jingyuan [1 ]
Tian, Ling [1 ,2 ]
Li, Kaiyang [3 ]
机构
[1] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China
[2] Shenzhen Inst Informat Technol, Inst Informat Technol, Shenzhen 518109, Peoples R China
[3] Georgia State Univ, Comp Sci Dept, Atlanta, GA 30302 USA
关键词
Complex systems; computational social science (CSS); event predictability; event prediction; Internet of Things (IoT)-based systems; PREDICTION; CONVERGENCE; INEQUALITY; SCIENCE;
D O I
10.1109/JIOT.2023.3247726
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Integrating massive social data with traditional social sciences, the computational social science (CSS) is crucial for understanding the Internet of Things (IoT)-based nondeterministic social systems. Event predictability is the fundamental premise of widespread societal event predictions with CSS. Due to data quality, model suitability, and the nondeterministic nature of IoT-based social systems, the event predictability is difficult to be characterized. Based on Turing computability and prediction error tolerability, this article posits a uniform event predictability theory. With discrepancy and Rademacher complexity, the generalization error bound is utilized to represent data quality and model suitability. Together with thresholds for the generalization error bound and confidence, the event predictability is modeled in a probabilistic manner to capture the nondeterminism within IoT-based social systems. The event predictability theory is theoretically proved and validated, and utilizing the proposed approximation algorithm for discriminating event predictability (AADEP), its applicability is further verified by experiments on a real-world data set for IoT-based nondeterministic social systems.
引用
收藏
页码:12496 / 12507
页数:12
相关论文
共 55 条
[1]   UNIFORM CONVERGENCE OF VAPNIK-CHERVONENKIS CLASSES UNDER ERGODIC SAMPLING [J].
Adams, Terrence M. ;
Nobel, Andrew B. .
ANNALS OF PROBABILITY, 2010, 38 (04) :1345-1367
[2]  
Bilos M, 2019, ADV NEUR IN, V32
[3]   What Do We Know When? Modeling Predictability of Transit Operations [J].
Buechel, Beda ;
Corman, Francesco .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (09) :15684-15695
[4]   DeepHawkes: Bridging the Gap between Prediction and Understanding of Information Cascades [J].
Cao, Qi ;
Shen, Huawei ;
Cen, Keting ;
Ouyang, Wentao ;
Cheng, Xueqi .
CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, 2017, :1149-1158
[5]   A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees [J].
De Caigny, Arno ;
Coussement, Kristof ;
De Bock, Koen W. .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2018, 269 (02) :760-772
[6]  
DelSole T, 2004, J ATMOS SCI, V61, P2425, DOI 10.1175/1520-0469(2004)061<2425:PAITPI>2.0.CO
[7]  
2
[8]   PROPAGATION OF UNCERTAINTIES IN DETERMINISTIC SYSTEMS [J].
DONG, WM ;
CHIANG, WL ;
WONG, FS .
COMPUTERS & STRUCTURES, 1987, 26 (03) :415-423
[9]  
Duan J., 2022, DIGIT COMMUN NETW
[10]  
Engels F., 1934, HE DUHRINGS REVOLUTI