Comparing quantified MAP-dependence to other measures of relevance in Bayesian networks
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2022-06-20
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en
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Abstract
In the fields of explainable AI and Bayesian networks, a recently introduced
concept is a measure called MAP-independence. It is meant to assist in the
justification of decisions made using decision support systems, by identifying
irrelevant intermediate variables in a decision problem. In this paper, we
argue that the binary nature of this measure is too crude, and may lead to
a large set of relevant variables, some of which only change the Maximum A
Posteriori outcome for very specific and maybe unlikely observations. We
hypothesise that “quantifying” the measure (meaning “to change it from a
binary measure to a measure on the [0,1] scale”) would allow us to identify
differences in how relevant each variable in a set of relevant variables is. We
name this new measure quantified MAP-dependence. We make an implementation
which is, to the best of our knowledge, the first implementation
of both MAP-independence and quantified MAP-dependence. Furthermore,
we apply these measures to the ALARM network. For comparison, we also
apply an older measure of relevance named intrinsic relevance. Based on the
results, we conclude the following: Firstly, that our hypothesis about the
usefulness of quantifying MAP-independence is true. Secondly, that compared
to intrinsic relevance, quantified MAP-dependence describes a fundamentally
different interpretation of what makes an intermediate variable
relevant.
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Faculteit der Sociale Wetenschappen