**5. Conclusion**

Figure 2), a major part of the variation space might be covered. Increasing numbers of fea‐ tures (i.e. dimensions) result in an exponentially growing number of theoretically existing feature combinations that are not linked to a target. In the present study a total of approx. 14 % of the variability space was not covered by any target (Table 3). In order to evaluate this non-assigned part of the variability space, let us assume a variable number of features *n* each consisting of three feature states, a number of targets that can be accommodatedby in‐ creasing with a factor of 2 with every additional feature, and one and only one state per fea‐

The resulting multidimensional spaces for a number of features ranging from 2 to 8, the number of targets accommodated and the resulting coverage are shown in Table 6. If more than one state of a feature can identify a target a larger coverage can be expected. This is the case in the here presented datamodel for the diagnosis of hormone treatment, since the probability to correctly classify all situations of hormone treatment was maximised. This is illustrated in Table 3. The high coverage of approx. 85.8% of the current model can be ex‐ plained by the situation that the model was optimised to find all occasions of illegal use of

> *n* **Combinations (3n ) Targets (2n ) coverage** 2 9 4 44% 3 27 8 30% 81 16 20% 243 32 13% 729 64 9% 2187 128 6% 6561 256 4%

**Table 6.** Relationship between the number of dimensions of a variability space (n), the possible number of combinations of feature states, and the coverage of the associated number of targets under the assumption of only

The development of a specific model for reaching a histological diagnosis in a general plat‐ form provides several constraints, such as the lack of automatically calculating the number of deviating features (feature 13) from the number of individually selected features of group III. The advantage of the current procedure is the strict framework which forces to analyse the information structure in detail, and generic tools are available for testing and evaluation.

hormones, i.e. the coverage of the classes "positive" was maximised.

(*Fi*,*<sup>k</sup>* ⇒*T <sup>p</sup>*) ∧(*Fi*,¬*<sup>k</sup>* ⇏*T <sup>p</sup>*) (13)

ture identifying a target *p*:

64 Decision Support Systems

Whereas equations (1) and (2) apply.

one state per feature identifying a target (equation (13)).

The presented parameters for redundancy, uniqueness, separation capability and coverage of variability space provide useful tools for the validation of a datamodel. The Developer as part of the Determinator system implements these parameters in an ordered manner, as exempli‐ fied in Table 5. The development and performance of the datamodel for reaching a diagno‐ sis of the treatment of veal calves with hormones in the framework of Determinator reveals that a specific model can be developed and applied successfully in a generic framework.
