# Significance of Single-Interval Discrete Attributes: Case Study on Two-Level Discretisation
## 5 Performed Experiments
### Standard Discretisation Approaches  
As stated before, discretisation involves a transformation of features that can result in an increase in performance, but also a decrease is possible; everything depends on the data and the selected discretisation algorithm. In standard approaches to the task, the same method is applied to all available features, and the popular opinion is that supervised procedures lead to better results than unsupervised ones. However, past studies have showed that, depending on the data, this bad reputation of the latter is not always deserved [25]. \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 F-writers & **93.34** & 91.60 & 89.79 & 85.56 & 91.16 \\ M-writers & **84.03** & 77.43 & 75.63 & 77.29 & 75.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average performance [%] of the selected inducers working on the datasets in the continuous input domain. Furthermore, in the case when several separate sets with the same features (such as training and test sets) are needed, the methodology of their transformations can play a significant role. Separate sets can be discretised entirely independently, and then intervals and cut points are constructed based only on the local context of each set. The other path of transformations leads through learning discrete data models from the training data, and then the formed intervals are enforced on the test data. In the research described, an independent transformation of all sets was used. For discretisation using supervised MDL methods, the performance of the classification systems considered is shown in Table 3. In the majority of cases, discretisation resulted in worsened accuracy--sometimes rather slight--but for some inducers, degradation was severe, which happened in consequence of data irregularities existing in the sets. Naive Bayes and Bayesian Net shared some similarities. Therefore, they reported close results, and they suffered the most. Some cases of improvement could be observed for the J48 and kNN classifiers, in particular for the male writer dataset. When the results from the Fayyad and Irani method were compared against those received when the Kononenko method was employed, the latter led to an overall higher accuracy. For the two unsupervised methods examined, the input parameter of a required number of intervals to be constructed was always studied in the range from 2 to 10, and nine variants of the data were explored. For equal width binning, the performance of all inducers was included in Table 4.  
NB classifier again suffered for both the female and male writer datasets, regardless of the number of bins considered for equal width binning, yet it was not so bad as in the case of supervised discretisation. For BNet, the results were varied depending on a number of intervals constructed and a dataset. For M-writers, mostly some improved performance was observed, while such a situation for F-writers happened only once when seven bins were formed. For J48, M-writers fared better than F-writers through discretisation. For kNN, the results showed some cases of improvement for the female writers, but for the male writer dataset, mostly worse recognition was noted. Random Forest worked surprisingly well (in comparison to other learners) when higher numbers of intervals were constructed. Table 5 lists the performance of the inducers for equal frequency binning. Both unsupervised discretisation methods can be compared against each other and measured against supervised approaches, but firstly a look back to the continuous domain was needed. For the male writers, NB always obtained worse results, but for the female writers in roughly half of the variants, some improvement was noted. BNet showed enhanced results only once for F-writers, yet for M-writers, it showed enhanced results in six out of nine cases.