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- W2969688331 abstract "This paper presents a framework based on the random Bayesian networks forest (RBNF) to assess situation, which consists of three main components: data processing, offline construction, and training of the Bayesian network. Single Bayesian network is constructed by combination of the K2 algorithm and genetic algorithm. In addition, in order to improve the ability of generalization and increase prediction accuracy in universal situations, bagging learning is used to eliminate redundant information and extract implicit dependences between features captured by various sensors. The proposed framework is evaluated and tested in a real Australia credit data. Simulation results show that the framework can provide sufficient performance for situation assessment in terms of accuracy, reliability and robustness." @default.
- W2969688331 created "2019-08-29" @default.
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- W2969688331 date "2019-08-22" @default.
- W2969688331 modified "2023-10-03" @default.
- W2969688331 title "Situation Assessment with Random Bayesian Network Forest" @default.
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- W2969688331 doi "https://doi.org/10.1007/978-981-32-9244-4_57" @default.
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