Discussion and Results Chapter: Calculated F-Measures, All of the Transformation Methods

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¶ … calculated f-measures, all of the transformation methods improved the performance of most classification methods, though in many cases performance did not vary considerably between untransformed (i.e. original) and transformed data or between the varied transformations and classification methods that were applied to the various data sets. There are certain notable exceptions, in particular data sets for which specific classification systems seemed wholly unsuited regardless of any transformation method applied, and also a few cases in which a transformation method greatly improved the performance of a classification method. The NTP2 data set and the DARPA data set both resulted in lower overall performance across transformation and classification methods, yet there is a substantial spread in the performance of transformation and classification methods.

For the NTP2 data set, the MLP classification system was the strongest performer overall, and performance was significantly improved for several classification systems through use of the PPFSCADA transformation method -- in the case of the SVM classification method, performance was almost doubled following this transformation. The PPFSCADA transformation method generally had greatest positive impact on performance across data sets and transformation methods, though it actually greatly reduced performance when the 7NN classification method was applied to the NTP2 data set. Performance on the DAPRA set was somewhat higher on average than the NTP2 data set, though the MLP classification method showed especially poor performance with this… [END OF PREVIEW]

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