I would like to be able to process the data I'm using by weighting it. The data that I am working with contains both very high quality cases and some that are not as accurate. When modeling this data, I would like to place more importance on the accuracy of the model for the high quality data but not neglect the low quality data that I have. Initially, I did this by assigning a weighting factor to each test case. I then used this weighting factor to scale the residual from the model for each case, which lead to an R-square fitness value that drove the regression model to focus on the best quality data. I would like to use GeneXproTools to model my data, but I still need to weight the data. Is there a way to do this? Any suggestions would be appreciated.
The simplest way of doing this consists of duplicating a certain number of times the high quality sample cases, thus giving more clout to these sample cases in model design. Another alternative would be the creation of a custom fitness function where you could take into account your “weighting factor”.