What makes CFAR-m different from the competition

1. CFAR-m Competitors
Objectivity No external weighting or Unique to CFAR-m
data manipulation is made:
the weighting schema is This feature is a major step
extracted from the data forward and a key
content and variables’improvement over existing
internal dynamics. methods in terms of
aggregation and ranking
Ranking vs Makes automatically the Unique to CFAR-m
Clustering ranking items on the basis of
complex data without any
external manipulation (the
ranking depends only on the
data themselves =
endogenous method)
Specificity A specific equation Unique to CFAR-m
calculates each individual
indicator
Decision support allows simulations and then Unique to CFAR-m
proposes to decision makers
various plans of action and
optimal sequence of reform
Contribution to the For each variable CFAR-m Unique in ranking
ranking provides its contribution to This is a powerful tool to the
the ranking selection of relevant variables
allowing a better analyse
phenomena
Capture of the non As CFAR-m uses Neural Very interesting in data
linearity network it can capture the analysis
non linearity of the relation This is a very interesting
between variables. combined with all the other
features.
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