Performance was increased by the PPFSCADA transformation method to the greatest degree, and the MLP classification method was similarly the highest performer across a greater number of data sets than other classification methods, though not to as significant degree as PPFSCADA outperformed other transformation methods, on average. Given these results, it is necessary to question the benefits of conducting additional transformation processing on data sets that show high performance without any transformation performed; depending on the resource and time intensity of the additional transformation, the benefit achieved by such...
On the other hand, poor performance in pre-transformation analysis can be greatly improved through transformation in some cases, and multiple transformations might be necessary to achieve the best results.
Further research into the nature of particular data sets in relation to the performance of these transformation methods and classification methods is recommended in order to achieve greater efficiency in selecting appropriate methodologies in future analyses.
