Abstract
This study investigates the effectiveness of advanced computational intelligence techniques in detecting adverse events in Afghanistan. The study first applies feature reduction techniques to identify significant variables. Then it uses five cost-sensitive classification methods. Finally, the study reports the resulting classification accuracy rates and areas under the receiver operating characteristics charts for adverse events for each method for the entire country and its seven regions. It appears that when analysis is performed for the entire country, there is little correlation between adverse events and project types and the number of projects. However, the same type of analysis performed for each of its seven regions shows a connection between adverse events and the infrastructure budget and the number of projects allocated for the specific regions and times. Among the five classifiers, the C4.5 decision tree and k-nearest neighbor seem to be the best in terms of global performance.
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Acknowledgment
This study was supported in part by Grant no. 10523339, Complex Systems Engineering for Rapid Computational Socio-Cultural Network Analysis, from the Office of Naval Research awarded to Dr. W. Karwowski at the University of Central Florida.
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Zurada, J., Shi, D., Karwowski, W., Guan, J., Çakıt, E. (2019). Detecting Adverse Events in an Active Theater of War Using Advanced Computational Intelligence Techniques. In: Aliev, R., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Sadikoglu, F. (eds) 13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing — ICAFS-2018. ICAFS 2018. Advances in Intelligent Systems and Computing, vol 896. Springer, Cham. https://doi.org/10.1007/978-3-030-04164-9_121
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DOI: https://doi.org/10.1007/978-3-030-04164-9_121
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