# Significance of Single-Interval Discrete Attributes: Case Study on Two-Level Discretisation
## 5 Performed Experiments
### Standard Discretisation Approaches
J48 recorded mostly enhanced results for the male writers, but for the female writers, it happened rarely. The performance of kNN was more often worse than better, and the opposite was true for the RndF classifier. Again, the improvement here was more noticeable than for other inducers. When the unsupervised methods were compared against each other, it turned out that the resulting performance was often better for equal frequency binning. It confirmed some \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{5}{c}{**Inducer**} \\ \cline{2-6}  & **NB** & **BNet** & **J48** & **kNN** & **RndF** \\ \hline \multicolumn{6}{c}{[PERSON] and [PERSON] (dsF) method} \\ \hline F-writers & 50.00 & 50.00 & **87.78** & 62.22 & 62.22 \\ M-writers & 69.44 & 67.22 & 80.76 & **83.54** & 77.22 \\ \hline \multicolumn{6}{c}{Kononenko (dsK) method} \\ \hline F-writers & 62.22 & 62.22 & **93.40** & 62.22 & 74.24 \\ M-writers & 68.89 & 67.22 & 80.76 & **82.43** & 76.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance [%] of inducers for the datasets discretised by the standard supervised Fayyad and Irani (dsF) and Kononenko (dsK) algorithms. initial expectations and popular opinions. For most of the inducers, some numbers of bins constructed led to higher accuracy. For the female and male writer datasets, the discretisation method and, when applicable (as for unsupervised algorithms), the value of the input parameter, which led to the best performance for each studied classifier, were analysed. They are presented in the section dedicated to the analysis of the obtained research results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Nr of**} & \multicolumn{5}{c}{**Inducer**} \\ \cline{3-8}
**Bins** & **Dataset** & **NB** & **BNet** & **J48** & **kNN** & **RndF** \\ \hline
02 & F-writers & 87.02 & 87.50 & 85.35 & **88.06** & 86.32 \\  & M-writers & 73.82 & **73.96** & 72.71 & 70.14 & 73.68 \\ \hline
03 & F-writers & 89.38 & **91.60** & 83.68 & 85.84 & 87.92 \\  & M-writers & 78.68 & **81.60** & 77.29 & 72.36 & 76.32 \\ \hline
04 & F-writers & **91.67** & 89.24 & 86.95 & 82.78 & 90.97 \\  & M-writers & 80.63 & 82.92 & 79.24 & 71.32 & **84.52** \\ \hline
05 & F-writers & 90.56 & **91.11** & 80.42 & 84.10 & 89.86 \\  & M-writers & 79.38 & **79.59** & 75.77 & 69.52 & 76.32 \\ \hline
06 & F-writers & **92.22** & 88.06 & 85.70 & 85.21 & 89.24 \\  & M-writers & **82.22** & 79.79 & 75.21 & 77.22 & 82.16 \\ \hline
07 & F-writers & 91.11 & **93.41** & 89.66 & 86.46 & 92.85 \\  & M-writers & 81.04 & 77.57 & 71.95 & 72.29 & **81.74** \\ \hline
08 & F-writers & 91.67 & 88.27 & 90.97 & 85.77 & **92.78** \\  & M-writers & 77.64 & 81.39 & 75.42 & 73.61 & **82.22** \\ \hline
09 & F-writers & 90.56 & 91.74 & 90.35 & 88.68 & **94.59** \\  & M-writers & 81.53 & 76.74 & 76.46 & 74.59 & **85.14** \\ \hline
10 & F-writers & 91.67 & 86.95 & 87.02 & 86.32 & **93.96** \\  & M-writers & 80.00 & 80.77 & 76.25 & 73.61 & **81.04** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance [%] of inducers for the datasets discretised by the standard unsupervised equal width binning.