  With the widespread use of machine learning to support decision-making, it is
increasingly important to verify and understand the reasons why a particular
output is produced. Although post-training feature importance approaches assist
this interpretation, there is an overall lack of consensus regarding how
feature importance should be quantified, making explanations of model
predictions unreliable. In addition, many of these explanations depend on the
specific machine learning approach employed and on the subset of data used when
calculating feature importance. A possible solution to improve the reliability
of explanations is to combine results from multiple feature importance
quantifiers from different machine learning approaches coupled with
re-sampling. Current state-of-the-art ensemble feature importance fusion uses
crisp techniques to fuse results from different approaches. There is, however,
significant loss of information as these approaches are not context-aware and
reduce several quantifiers to a single crisp output. More importantly, their
representation of 'importance' as coefficients is misleading and
incomprehensible to end-users and decision makers. Here we show how the use of
fuzzy data fusion methods can overcome some of the important limitations of
crisp fusion methods.
