# Significance of Single-Interval Discrete Attributes: Case Study on Two-Level Discretisation
## 5 Performed Experiments
### Two-Level Discretisation
For the F-writers, the Kononenko method used as a base always led to better results than the Fayyad and Irani algorithm: dsK-duf and dsK-duw resulted in better performance of the BNet than that obtained for dsF-duf and dsF-duw, regardless of the number of bins constructed in  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Number of Bins in Test Sets**} \\ \cline{2-10}
**ds-du** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline \multicolumn{10}{c}{F-writers dataset} \\ \hline dsF-duf & 50.00 & 61.39 & 74.31 & 81.18 & 83.47 & 86.94 & 89.38 & 89.38 & 90.49 & **91.04** \\ \hline dsF-duw & 50.00 & 57.29 & 68.40 & 75.63 & 82.85 & 86.39 & 89.93 & **92.22** & **92.22** & **92.22** \\ \hline dsK-duf & 62.22 & 79.17 & 85.76 & 88.19 & 90.42 & 91.60 & **92.22** & **92.22** & **92.22** & **92.22** \\ \hline dsK-duw & 62.22 & 71.25 & 85.21 & 90.42 & 91.67 & 92.22 & 92.78 & **93.40** & **93.40** & **93.40** \\ \hline \multicolumn{10}{c}{M-writers dataset} \\ \hline dsF-duf & 67.22 & 71.67 & 74.65 & 77.22 & 77.78 & 77.78 & **77.85** & 77.29 & 76.67 & **77.85** \\ \hline dsF-duw & 67.22 & 73.61 & 77.85 & **79.72** & 79.72 & 77.29 & 78.47 & 79.10 & 79.10 & 78.47 \\ \hline dsK-duf & 67.22 & 71.67 & 74.65 & 77.22 & 77.78 & 77.78 & **77.85** & 77.29 & 76.67 & **77.85** \\ \hline dsK-duw & 67.22 & 73.61 & 77.85 & **79.72** & 79.72 & 77.29 & 78.47 & 79.10 & 79.10 & 78.47 \\ \additional second-level transformations. However, for the M-writers, the visible trend depended on the unsupervised method--for equal width binning combined with either of the supervised discretisation processes, the accuracy was higher than for equal frequency binning. For the remaining classifiers, the performance was much more varied, as shown in the plots in Figures 2-5. In each chart, the categories describing the horizontal axis correspond to the number of bins for the additionally transformed variables in the training sets, while the series was defined by the numbers of bins in the test sets. The top four plots are for F-writers, and the bottom ones for the male writer datasets. Combinations of discretisation methods are commented on in the chart titles. For the NB classifier (Figure 2), the trends indicate that for the female writers, some similarities could be observed with respect to the performance of BNet. It is visible that additional transformations of variables in the test sets brought more noticeable results than when the training sets were subjected to the second level of discretisation. Such a statement for the M-writers would not be true; here, differences were visible for both directions. Figure 2: _Cont._  
For both the female and male writer datasets, additional processing of variables caused enhanced performance in many cases but rarely for the higher ranges of bin numbers. In particular, for M-writers, fewer bins led to enhanced predictions, while more intervals brought worsened accuracy. For F-writers, the trend lines were much clearer and showed that for the Fayyad-based data variants, increasing the number of bins in the test sets above one caused enhanced results. For the Kononenko-based versions, the opposite situation was observed. For M-writers, the changes occurred in both directions. An analysis of the plots for J48 (Figure 3) resulted in the conclusion that for the female writers the trends visible for categories were very similar, with hardly any differences. It indicated a higher degree of dependence on transformations of the test sets than of the training sets. For the male writers, much more variation was noticed, with yet again many cases of improved predictions. As previously for NB, for F-writers, closer similarities were observed between the plots based on the same supervised discretisation method, while for M-writers, the closeness depended on the unsupervised method employed.