Abstract
The purpose of this paper was to provide help for poison identification via the Bayesian network according to the observed preliminary symptoms of the poisoning people. We proposed a novel improvement on K2 algorithm to solve the problem of the lack of data under the special context. Determining initial node sequence of K2 algorithm via Markov blanket, we improved greatly Bayesian network structure learning with small datasets. Bootstrap data expansion and Gibbs data correction combining with maximum weight spanning tree (MWST) were used to expand the original small data set to further improve the performance and reliability of the structure learning. Then we applied this kind of combination scheme into a real data set to verify its validity and reliability. Finally we were able to quickly distinguish between a variety of biochemical reagents with this method, and the result of the inference can be used to guide emergency rescue after certain biochemical terrorism attack.
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Jiang, J., Wang, J., Yu, H., Xu, H. (2013). Poison Identification Based on Bayesian Network: A Novel Improvement on K2 Algorithm via Markov Blanket. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38715-9_21
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DOI: https://doi.org/10.1007/978-3-642-38715-9_21
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