# EvoSplit: An Evolutionary Approach to Split a Multi-Label Data Set into Disjoint Subsets
## 5 Evaluation of Classification  
Next, this work evaluates the effect that the distribution of examples in a multi-label data set into subsets has on the classification metrics. Similarly to [13], the evaluation is carried out employing two standard multi-label classification algorithms: Binary Relevance (BR) and Label Powerset (LP). The results of the classification are measured using recall, precision, \(F_{1}\) score and Jaccard index. These metrics are calculated using both micro-average (i.e., calculate metrics globally by counting the total true positives, false negatives and false positives) and macro-average (i.e., calculate metrics for each label, and then their unweighted mean). The variance in the classification across different folds is also analyzed, which provides information about the generalization stability of the different approaches. Following [12; 13], the results are discussed based on the average ranking of the different methods. The best method for a particular measure obtains a rank of 1, the next \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & LD & LPD & FZ & FLZ \\ \hline
**emotions** & \(\mathbf{5.28\times 10^{-3}}\) & \(7.43\times 10^{-3}\) & 0 & **0** \\ \hline
**scene** & \(\mathbf{1.77\times 10^{-3}}\) & \(\mathbf{2.18\times 10^{-2}}\) & 0 & **0** \\ \hline
**genbase** & \(\mathbf{4.70\times 10^{-3}}\) & \(\mathbf{1.42\times 10^{-2}}\) & 10 & **74** \\ \hline
**medical** & \(3.39\times 10^{-3}\) & \(\mathbf{1.19\times 10^{-2}}\) & 10 & 179 \\ \hline
**yeast** & \(\mathbf{6.56\times 10^{-4}}\) & \(\mathbf{4.80\times 10^{-4}}\) & 0 & **0** \\ \hline
**enron** & \(\mathbf{8.28\times 10^{-4}}\) & \(\mathbf{4.60\times 10^{-4}}\) & 10 & **58** \\ \hline
**rcv1 subset4** & \(\mathbf{2.93\times 10^{-4}}\) & \(\mathbf{2.04\times 10^{-4}}\) & 10 & 71 \\ \hline
**rcv1 subset3** & \(\mathbf{2.93\times 10^{-4}}\) & \(\mathbf{1.87\times 10^{-4}}\) & 10 & **42** \\ \hline
**rcv1 subset5** & \(\mathbf{2.70\times 10^{-4}}\) & \(\mathbf{1.81\times 10^{-4}}\) & 10 & **47** \\ \hline
**rcv1 subset2** & \(\mathbf{2.96\times 10^{-4}}\) & \(1.89\times 10^{-4}\) & 10 & 36 \\ \hline
**rcv1 subset1** & \(\mathbf{2.94\times 10^{-4}}\) & \(\mathbf{1.68\times 10^{-4}}\) & 10 & **50** \\ \hline
**tmc2007\_500** & \(\mathbf{1.27\times 10^{-4}}\) & \(\mathbf{1.89\times 10^{-4}}\) & 0 & **0** \\ \hline
**bibtex** & \(\mathbf{2.68\times 10^{-4}}\) & \(\mathbf{1.53\times 10^{-4}}\) & 0 & **0** \\ \hline
**Corel5k** & \(\mathbf{2.84\times 10^{-4}}\) & \(\mathbf{1.41\times 10^{-4}}\) & 10 & **907** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Evaluation of the MOEA approach without considering the constraint. In bold the results that are better than the Stratification method. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & LD & LPD & FZ & FLZ \\ \hline
**genbase** & \(5.12\times 10^{-3}\) & \(\mathbf{1.40\times 10^{-2}}\) & 10 & **73** \\ \hline
**medical** & \(\mathbf{2.96\times 10^{-3}}\) & \(\mathbf{1.19\times 10^{-2}}\) & 10 & 175 \\ \hline
**enron** & \(\mathbf{8.29\times 10^{-4}}\) & \(\mathbf{4.63\times 10^{-4}}\) & 10 & **53** \\ \hline
**rcv1 subset4** & \(\mathbf{3.11\times 10^{-4}}\) & \(\mathbf{2.06\times 10^{-4}}\) & 10 & **61** \\ \hline
**rcv1 subset3** & \(\mathbf{2.70\times 10^{-4}}\) & \(\mathbf{1.85\times 10^{-4}}\) & 10 & **38** \\ \hline
**rcv1 subset5** & \(\mathbf{2.69\times 10^{-4}}\) & \(\mathbf{1.84\times 10^{-4}}\) & 10 & **45** \\ \hline
**rcv1 subset2** & \(\mathbf{2.87\times 10^{-4}}\) & \(1.89\times 10^{-4}\) & 10 & **31** \\ \hline
**rcv1 subset1** & \(\mathbf{2.54\times 10^{-4}}\) & \(\mathbf{1.63\times 10^{-4}}\) & 10 & **41** \\ \hline
**Corel5k** & \(\mathbf{2.52\times 10^{-4}}\) & \(\mathbf{1.39\times 10^{-4}}\) & 10 & **841** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Evaluation of the MOEA approach considering the constraint. In bold the results that are better than the Stratification method. one a rank of 2, and so on. Figures 3 and 4 show the evaluations for both classification algorithms, BR and LP.