Abstract
Arson-caused wildfires have a rate of clarification that is extremely low compared to other criminal activities. This fact made evident the importance of developing methodologies to assist investigators in the criminal profiling. For that we introduce Bayesian Networks (BN), which have only recently be applied to criminal profiling and never to arsonists. We learn a BN from data and expert knowledge and, after validation, we use it to predict the profile (characteristics) of the offender from the information about a particular arson-caused wildfire, including confidence levels that represent expected probabilities.
R. Delgado—This author is supported by Ministerio de Economía y Competitividad, Gobierno de España, project ref. MTM2015 67802-P.
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Acknowledgments
The authors wish to thank the anonymous referees for careful reading and helpful comments that resulted in an overall improvement of the paper. They also would express their acknowledgment to the Prosecution Office of Environment and Urbanism of the Spanish state for providing data and promote research.
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Delgado, R., González, J.L., Sotoca, A., Tibau, XA. (2016). A Bayesian Network Profiler for Wildfire Arsonists. In: Pardalos, P., Conca, P., Giuffrida, G., Nicosia, G. (eds) Machine Learning, Optimization, and Big Data. MOD 2016. Lecture Notes in Computer Science(), vol 10122. Springer, Cham. https://doi.org/10.1007/978-3-319-51469-7_31
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