Creation of relevance values for each factor

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The evaluation of an acceptable example (as the candidate) ends with generating the intermediate result vector. Afterwards, the procedure switches over to the next acceptable example that has not been used as a candidate example yet. Successively all acceptable variants serve as a candidate example and the rest of the examples become reference examples. Every candidate differs in another way from ideal and therefore gives different information on the relevance of the factors. The counter matrix is initiated again and adaption cycles begin until the intermediate result vector belonging to each candidate is found.
To each candidate example the FACTORFINDER-Procedure generates after many adaption cycles a well-founded tolerance vector. Each well-founded tolerance vector is transformed into one intermediate result vector. The intermediate result vector represents properties of the relationship between candidate example and reference examples.
All intermediate result vectors are fused to one single relevance vector. The FACTORFINDER-Relevance is the average of all intermediate results of a factor. The FACTORFINDER-Relevance vector is the combination of all FACTORFINDER-Relevance values. The more similar the variants are the lower the relevance. Strongly differing variants make the factors look little tolerable which leads to high relevance values.
Each example base leads to exactly one relevance vector. It shows the importance of the factors. From an example base as many intermediate result vectors can be derived as the example base contains successful examples. This follows from using each acceptable variant as candidate example and generating the intermediate result vector by comparing it with all the other acceptable examples. From the average of all intermediate results the FACTORFINDER-Relevance vector is created.

Supposed there are 100 acceptable variants as example base

then there are 100 well-founded tolerance vectors,

there are 100 intermediate result vectors based on the well-founded tolerance vectors

and one FACTORFINDER-Relevance vector based on the 100 intermediate result vectors.

For illustration an example base of 10 alternatives is presented. The following table shows in each row an intermediate result for the examples 1 to 10. The bottom line shows the FACTORFINDER-Relevance which is the average of all intermediate results.

	Factor 1	Factor 2	Factor 3	Factor 4	Factor 5
Example 1	1.0	.5	.0	.0	.7
Example 2	.7	.0	.4	.0	.9
Example 3	1.0	.2	.4	.8	.0
Example 4	1.0	.0	.0	.0	1.0
Example 5	.0	.5	.8	.7	.7
Example 6	.7	.3	.3	.6	.0
Example 7	1.0	.0	.0	.0	.0
Example 8	.0	.0	.0	.0	.0
Example 9	.8	.0	.0	.7	.6
Example 10	.6	.0	.0	.0	.4
FF-Relevance	.7	.2	.2	.3	.4

The evaluation suggests to concentrate the available resources to factor 1. This factor is the most important (0.7) to reach the target criterion.