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Determination of variables for a Bayesian network and the most precious one. (English) Zbl 1452.68148

Carvalho, Joao Paulo (ed.) et al., Information processing and management of uncertainty in knowledge-based systems. 16th international conference, IPMU 2016, Eindhoven, The Netherlands, June 20–24, 2016. Proceedings. Part I. Cham: Springer. Commun. Comput. Inf. Sci. 610, 313-325 (2016).
Summary: To ensure the quality of a learned Bayesian network out of limited data sets, evaluation and selection process of variables becomes necessary. With this purpose, two new variable selection criteria \(N_2S_j\) and \(N_3S_j\) are proposed in this research which show superior performance on limited data sets. These newly developed variable selection criteria with the existing ones from prior research are employed to create Bayesian networks from three different limited data sets. On each step of variable elimination, the performance of the resulting BNs are evaluated in terms of different network performance metrics. Furthermore, a new variable evaluation criteria, \(IH_j\), is proposed which measures the impact of a variable to all the other variables in the network. \(IH_j\) serves as an indicator of the most important variables in the network which has a special importance for the use of BNs in social science research, where it is crucial to identify the most important factors in a setting.
For the entire collection see [Zbl 1385.68005].

MSC:

68T05 Learning and adaptive systems in artificial intelligence
62H22 Probabilistic graphical models

Software:

GeNIe; bnlearn
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Full Text: DOI

References:

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