the variable selection of the frameworks can be performed for each analyte (response
variable) separately or for all analytes together. For example, the variable
selection by the parallel growing neural network was performed for each analyte
separately in section 8.4.1 by using a separate ranking
for both analytes, whereas the variable selection in the sections 9.1.2,
9.2.4, 9.3.2 and 9.3.3
was performed for all analytes together by using a combined ranking for all
analytes together (addition of the individual rankings). In the second step
of the iterative variable addition procedure, the combined ranking saves about
n times the computing time for
n analytes. Yet the question is,
if a separate variable selection performs significantly better.
the ternary mixtures of methanol, ethanol and 1-propanol measured by SPR and
evaluated by the parallel growing network framework (see section
9.2.4) were evaluated again using a separate ranking for each analyte and
separate iterative addition steps (step 2 of the framework). The RMSE of the
validation data, which are shown in table 10, are
very similar and do not allow a clear decision which method to prefer.
table 10: Relative RMSE in %
for the prediction of the validation data measured by SPR and evaluated by the
parallel growing neural network framework.
the parallel growing network framework of the refrigerant data together instead
of separately (compare with 4th row of table
4 in chapter 8) resulted in slightly worse predictions
of 2.08% for the validation data of R22 instead of 2.04% and slightly better
predictions for R134a with 2.53% instead of 2.61%. For other data sets, also
no clear preference can be seen rendering the more computing intensive separate
variable selection superfluous.