## 3 Results
### Second Experiment  
In the second experiment, the efficiency of examined methods was compared, where the parameterisation was the same as in the first experiment and was presented at the beginning of chapter 3. Results. In this stage of the research, the algorithms were examined on the imbalanced data sets built on the basis of the known set of data concerning occupancy detection [10]. The first step of this research stage consisted in establishing the maximal effective number of data particles, on which the GD and UGD methods can base in the classification process of individual imbalanced data sets. Established values were presented in Table 5.  
Table 6 presents the values of F-measure obtained by the 1 CT1P approach, as well as the GD and UGD methods, which divided individual data sets into data particles with values given in Table 5.  
Data included in Table 6 show that the UGD algorithm achieves the highest values of F-measure on eight analysed imbalanced data sets created on the basis of the real occupancy data set. The GD approach reaches the highest F-measure value on one data set (occupancy_25), similar as the 1 CT1P method (occupancy_35). The Unequal Geometrical \begin{table}
\begin{tabular}{c c} \hline
**Data Set** & **Target Number of Data Particles** \\ \hline occupancy\_12 & 256 \\ occupancy\_13 & 32 \\ occupancy\_14 & 256 \\ occupancy\_15 & 256 \\ occupancy\_23 & 64 \\ occupancy\_24 & 256 \\ occupancy\_25 & 256 \\ occupancy\_34 & 32 \\ occupancy\_35 & 64 \\ occupancy\_45 & 256 \\ \hline \end{tabular}
\end{table} 
Table 5: Numbers of data particles applied by the GD and UGD methods in classification process of individual imbalanced data sets concerning occupancy detection. Figure 6: A cumulative sum of the function derivative for the subsequent analysed numbers of data particles. Divide reaches the F-measure mean value of 0.805. The Geometrical Divide approach obtains insignificantly lower F-measure values, whose mean result equals 0.797. The approach of data particle creation based on class by a compound of \(1\div 1\) cardinality achieves the lowest results on nine two-dimensional variants of imbalanced occupancy data sets. In conducted experiment the mean F-measure value for 1 CT1P amounted to 0.740. It should be emphasized that higher results cannot be reached by the GD method because in this experiment it uses the deepest for itself divide of data particles. On the other hand, the next divide within the UGD method do not bring a significant increase of F-measure values. The obtained results were verified by a statistical test. Using the STAC tool [25], the non-parametric Friedman Aligned Ranks test [26] with Holm post-hoc multiple comparison [27] was conducted with the significant level \(\alpha\) = 0.05. The results were presented in Table 7.  
Based on the information presented in Table 7, it can be stated that the methods of data particles geometrical divide which apply the line passing through two points significantly outperform the 1 CT1P approach in the occupancy detection process that is grounded on the imbalanced data set. On the other hand, there is no significant statistical difference between the results obtained by the Geometrical Divide and the Unequal Geometrical Divide methods. Analysing the values of precision and recall obtained by individual approaches, it can be stated that the UGD obtained the highest precision on nine data sets, whereas in the criterion of recall the UGD reached the highest result on two data sets-occupancy_23 and occupancy_35. However, it should be mentioned that on the occupancy_23 all approaches obtained the same value, and on the occupancy_35 UGD and GD achieved the identical level of recall. The best performance in the aspect of recall value was obtained by the GD approach, which scored the highest result on nine data sets, including the afore-mentioned two data sets. By comparing the differences between the obtained precision and recall by each approach on the individual data sets, Table 8 was created.