Challenge: Where is the Impact of Bayesian Networks in Learning?
N. Friedman, D. Heckerman, M. Goldszmidt, and S. Russell
Proc. Fifteenth International Joint Conference on Artificial Intelligence (IJCAI). 1997. .
In recent years, there has been much interest in learning
Bayesian networks from data. Learning such models is desirable simply
because there is a wide array of off-the-shelf tools that can apply
the learned models as
expert systems, diagnosis engines, and decision support
systems. Practitioners also claim that adaptive Bayesian networks have
advantages in their own right as a non-parametric method for density
estimation, data analysis, pattern classification, and modeling.
Among the reasons cited we find: their semantic clarity and
understandability by humans, the ease of acquisition and incorporation
of prior knowledge, the ease of integration with optimal
decision-making methods, the possibility of causal interpretation of
learned models, and the automatic handling of noisy and missing data.
In spite of these claims,
and the initial success reported recently,
methods that learn Bayesian networks have
yet to make the impact that other techniques such as neural networks
and hidden Markov models have made in applications such as pattern
and speech recognition. In this paper, we challenge the
research community to identify and characterize domains where
induction of Bayesian networks makes the critical difference, and to
quantify the factors that are responsible for that difference. In
formalizing the challenge, we identify research
problems whose solution is, in our view, crucial for meeting this
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