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Data Stream Models for Predicting Adverse Events in a War Theater

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Title:Data Stream Models for Predicting Adverse Events in a War Theater
Authors:Shi, Donghui
Zurada, Jozef
Karwowski, Waldemar
Guan, Jian
Keywords:Data, Text, and Web Mining for Business Analytics
Decision Analytics, Mobile Services, and Service Science
Active War Theater, Adverse Events Prediction, Data Stream Models
Date Issued:08 Jan 2019
Abstract:Predicting adverse events in a war theater has been an active area of research. Recent studies used machine learning methods to predict adverse events utilizing infrastructure development spending data as input variables. The goals of these studies were to find correlation and disclose the main factors between adverse events and human-social-infrastructure development projects, and reduce the occurrence of the adverse events. The predictions still have large errors compared with the real values using the existing methods. The reason could be that some significant variables are removed to comply with constraints in a soft computing model such as neural networks, fuzzy inference systems (FIS) and adaptive neuro-fuzzy inference systems (ANFIS) that work well with a smaller number of variables. In this paper, a data stream approach using three data stream regression algorithms, AMRules, TargetMean and FIMTDD, is proposed to predict the adverse events so that much more input variables could be included. The results show that the data stream methods generate better results than machine learning methods used in the previous studies, thus helping us better understand the relationship between infrastructure development and adverse events. In addition the data stream methods also outperform the traditional linear regression model. An important advantage in using data stream methods is the ability to create and apply predictive models with a relatively small amount of memory and time. Finally, the use of data stream methods provides an additional advantage by allowing the user to observe error distribution over time for more accurate assessment of the performance of the resulting models.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/59556
ISBN:978-0-9981331-2-6
DOI:10.24251/HICSS.2019.142
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Data, Text, and Web Mining for Business Analytics


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