Test-selection strategies to increase the number of MAP-independent variables
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2022-06-20
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en
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Abstract
In practical applications, decision support systems that motivate and
justify decisions are essential for a user to understand and accept the systems.
Recently, the concept of MAP-independence was introduced which
aimed to provide such justification for Bayesian networks by explicating the
relevant intermediate variables for decisions in MAP problems. Whereas
the concept of MAP-independence may work well to justify a decision when
the number of relevant variables is small, in this paper we argue that for
scenarios in which the number of relevant variables is large, these justifications
would be inadequate and a user can only gain limited insight into the
decision. Therefore, we focus on finding test-selection strategies that can
decrease the number relevant variables in the network by deciding which
variable to gather evidence for in order to obtain a decision that is better
to justify and motivate. Specific test-selection strategies that we investigate
include the in-degree, out-degree and total degree of a variable, the distance
of a variable to the explanation variable, the expected utility, the expected
Gini index and mutual information with the explanation variable. After
running systematic simulations on the ALARM network and analysing the
results based on rank- and value-approximation, we conclude that the distance
to the explanation variable, expected utility, expected Gini index and
mutual information could serve as good test-selection strategies to decrease
the number of relevant variables. However, more research is needed to be
able to generalize these findings to a larger population of Bayesian networks.
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Faculteit der Sociale Wetenschappen