Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Upcoming SlideShare
×

CFAR-m

423 views

Published on

• Full Name
Comment goes here.

Are you sure you want to Yes No
Your message goes here
• Be the first to comment

• Be the first to like this

CFAR-m

1. 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