# Significance of Single-Interval Discrete Attributes: Case Study on Two-Level Discretisation
## 6 Discussion of Obtained Results
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & & \multicolumn{10}{c}{**Inducer**} \\ \cline{2-13}  & **NB** & **BNet** & **J48** & **kNN** & **RndF** & **NB** & **BNet** & **J48** & **kNN** & **RndF** \\ \hline Statistics & F-writers, equal frequency & binning (dsK-duf) & F-writers, equal width binning & (dsK-duw) \\ \hline Avg & 70.47 & 86.62 & 90.39 & 78.01 & 87.00 & 70.35 & 86.60 & 90.19 & 74.60 & 86.82 \\ St.dev. & 04.83 & 09.05 & 01.03 & 07.22 & 06.95 & 07.34 & 10.40 & 01.37 & 07.77 & 06.99 \\ Min & 62.22 & 62.22 & 89.51 & 56.88 & 63.13 & 55.07 & 62.22 & 88.96 & 50.00 & 63.13 \\ Max & 81.32 & 92.22 & 93.40 & 88.96 & 92.85 & 87.43 & 93.40 & 93.40 & 90.14 & 93.47 \\ \(i\)-Train\({}_{Max}\) & 03 & 01-10 & 01-03 & 01 & 01 & 02 & 01-10 & 01-04 & 01 & 02 \\  & & & 05-07 & & & & & & & & \\ \(i\)-Test\({}_{Max}\) & 03 & 07-10 & 01 & 09-10 & 05-10 & 03 & 08-10 & 01 & 09-10 & 06 \\ \hline Statistics & M-writers, equal frequency binning & (dsK-duf) & M-writers, equal width binning & (dsK-duw) \\ \hline Avg & 64.76 & 75.60 & 76.51 & 69.03 & 76.09 & 67.78 & 77.06 & 77.20 & 71.80 & 73.77 \\ St.dev. & 07.23 & 03.36 & 04.95 & 06.03 & 06.30 & 08.03 & 03.69 & 04.27 & 06.88 & 07.78 \\ Min & 56.04 & 67.22 & 59.51 & 56.67 & 58.61 & 53.68 & 67.22 & 60.63 & 56.88 & 55.90 \\ Max & 81.11 & 77.85 & 82.71 & 84.72 & 83.40 & 81.11 & 79.22 & 85.14 & 82.43 & 83.89 \\ \(i\)-Train\({}_{Max}\) & 06-07 & 01-10 & 03, 10 & 02 & 03 & 04 & 01-10 & 06 & 01 & 04 \\ \(i\)-Test\({}_{Max}\) & 01 & 07, 10 & 03, 10 & 01 & 10 & 02 & 04-05 & 07 & 01 & 09 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Statistics of performance [%] for the inducers working on the datasets discretised by two-level transformations combining the supervised Kononenko (dsK) method with unsupervised approaches. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & & \multicolumn{10}{c}{**Inducer**} \\ \cline{2-13}  & **NB** & **BNet** & **J48** & **kNN** & **RndF** & **NB** & **BNet** & **J48** & **kNN** & **RndF** \\ \hline Statistics & F-writers, equal frequency binning & (dsF-duf) & F-writers, equal width binning & (dsF-duw) \\ \hline Avg & 67.75 & 79.76 & 89.47 & 75.54 & 80.83 & 68.15 & 78.72 & 89.18 & 72.27 & 79.64 \\ St.dev. & 06.80 & 13.22 & 00.97 & 09.71 & 12.06 & 08.51 & 14.66 & 00.89 & 10.52 & 13.37 \\ Min & 50.00 & 50.00 & 07.78 & 50.00 & 50.00 & 50.00 & 50.00 & 87.78 & 50.00 & 50.00 \\ Max & 78.26 & 91.04 & 91.11 & 89.03 & 92.85 & 80.63 & 92.2  
discretisation and combining supervised and unsupervised methods. When the characteristics of the input domain, as well as specific modes of operation of inducers, are taken into account, enhanced predictions can follow. Such increased efficiency in recognition is typically treated as the most important factor in machine learning tasks. The presented research methodology can be applied in any domain characterised by the continuous input space, where, after a supervised discretisation process, such attributes exist that are assigned a single categorical representation. The main goal of the proposed two-step procedure is to prevent the loss of information that occurs in such conditions. The algorithm causes increased complexity in the data preparation stage, because discretisation is closer adapted to the data. Instead of relatively straightforward transformations, one-step and uniform for all features, discretisation is performed in two subsequent stages. In the first step, some standard supervised algorithm is applied to all attributes. Then, instead of treating single-bin attributes as irrelevant, for the second step, they are retrieved from the set of available variables and subjected to unsupervised discretisation. If there are no such attributes, or when their subset is relatively small (compared to the entire set of available features), the extended processing may bring no visible gain.