Calculated F-Measures, All of the Discussion Chapter

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Discussion

Overall, when performance was already relatively high for a data set (and this tended to be relatively consistent across classification methods), the transformation methods did not show tremendous variance, though in certain cases of difficult data sets particular transformation methods did have significant impacts on the performance of specific classification methods. 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 a transformation might not be worthwhile and in many cases might not be significant enough to affect the outcome of other measures and analyses.

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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….....

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