  When machine learning supports decision-making in safety-critical systems, it
is important to verify and understand the reasons why a particular output is
produced. Although feature importance calculation approaches assist in
interpretation, there is a lack of consensus regarding how features' importance
is quantified, which makes the explanations offered for the outcomes mostly
unreliable. A possible solution to address the lack of agreement is to combine
the results from multiple feature importance quantifiers to reduce the variance
of estimates. Our hypothesis is that this will lead to more robust and
trustworthy interpretations of the contribution of each feature to machine
learning predictions. To assist test this hypothesis, we propose an extensible
Framework divided in four main parts: (i) traditional data pre-processing and
preparation for predictive machine learning models; (ii) predictive machine
learning; (iii) feature importance quantification and (iv) feature importance
decision fusion using an ensemble strategy. We also introduce a novel fusion
metric and compare it to the state-of-the-art. Our approach is tested on
synthetic data, where the ground truth is known. We compare different fusion
approaches and their results for both training and test sets. We also
investigate how different characteristics within the datasets affect the
feature importance ensembles studied. Results show that our feature importance
ensemble Framework overall produces 15% less feature importance error compared
to existing methods. Additionally, results reveal that different levels of
noise in the datasets do not affect the feature importance ensembles' ability
to accurately quantify feature importance, whereas the feature importance
quantification error increases with the number of features and number of
orthogonal informative features.
