1. TECHNOLOGY OVERVIEW
The profusion of information which we notice today in all fields can lead to misinterpretation
of results. So, the need of advanced mathematical and statistical tools proves essential in
order to extract relevant information. In this way, we find in all fields some complex
phenomena which can be traced only by a multitude of variables, each illustrating just one
feature and only all together can be accounted for. Also, often these variables retro-act by
feedback phenomena which is specific to each case. For a better appreciation,
comprehension, decision or arbitration and deliberation, analysts have a great need for
classification and arrangement of their data through the construction of an aggregate
synthetic variable granting a total mark for each individual (Firm, bank, financial assets,
country, area, patient, site,…) without loss of data. The traditional data analysis methods
employed in this field (ACP, AFC, AFM…) reach their limits and leave behind them many
insufficiencies.
Hence, comes the interest of our method which is based on a neuronal approach and which
provides immense advantages compared to the preceding ones as regards to its faculties. We
have succeeded in developing a new technique allowing the construction of an aggregate
indicator starting from a data base of several variables and with the assistance of the Self-
organizing Maps. Our method proceeds in three stages: First, CFAR-m realizes self-
organization of items into homogeneous subsets (clustering), through a learning process that
takes into account the positive and negative interactions. Second, an appropriate weights
vector is determined for each item. Finally, the weighting vectors are applied to original data
to calculate the composite indicator and make the overall ranking. The weighting system can
be characterized as objective since it emanates from the informational content of the
variables themselves and their internal dynamics. This last feature of the C-FAR method
represents an invaluable step forward and a real surpassing of what has been done so far in
terms of classification - aggregation, especially that it does not lose any information (the
traditional methods provide an estimate by smoothing data) and it can be especially adapted
to very broad data bases (of qualitative and/or quantitative nature). The CFAR-method solves
a major concern of the problems of aggregation in which the question of the importance of
each variable always arises. It breaks with the usual procedures of aggregation based on too
simplified assumptions and resulting in considering, for each variable, an identical weighting
for all the individuals in the sample. Then, the weighting released by the C-FAR method
emanates from the informational contents of the same variables and of their internal
dynamics. It thus avoids the adoption either of equiponderation or of weightings established
on hexogen criteria (dispersions of the variables at the entry, for example) or subjective
estimates (which result from the opinion of a certain number of experts in the field
concerned).
There are so many application fields covering all aspects: Economics, Social, Financial,
Insurance, Medical, Pharmaceuticals, Geo-strategic, Aerospace, Ecological, Marketing
Research etc.
www.cfar-m.com R&D and partnerships: remi@cfar-m.com
