Abstract
A new approach to learning Bayesian Network classifiers, called the OSF-Classification method is presented. Most approaches try and fit the network to data. The usual method is to incorporate the log-likelihood score. Analysis of this score shows that a good score could still lead to bad classifiers. The new approach, rather than trying to fit the network to data, scores the network according to its classification error. One major assumption is made, in that the parameters of the learnt network are the observed statistical frequencies of the data. This method is shown to perform well against standard Non-Bayesian learning methods.
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© 1998 Springer-Verlag Berlin Heidelberg
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Tahseen, T., Gillies, D.F. (1998). A new approach to learning Bayesian Network classifiers from data: Using observed statistical frequencies. In: Giunchiglia, F. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 1998. Lecture Notes in Computer Science, vol 1480. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0057463
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DOI: https://doi.org/10.1007/BFb0057463
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