# Exploiting Domain Knowledge to Address Class Imbalance in Meteorological Data Mining
## 5 Experiments and Results
### Algorithm Runs
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Classifier**} \\ \hline
**Strategy** & **DT** & **kNN** & **MP** & **NB** & **RIP** \\ \hline UN & Mean & 0.728 & 0.711 & 0.786 & 0.773 & 0.586 \\ \hline  & SD & 0.011 & 0.009 & 0.009 & 0.008 & 0.014 \\ \hline U30 & Mean & 0.727 & 0.734 & 0.795 & 0.773 & 0.665 \\ \hline  & SD & 0.009 & 0.009 & 0.009 & 0.008 & 0.012 \\ \hline U50 & Mean & 0.737 & 0.759 & **0.803** & **0.774** & **0.732** \\ \hline  & SD & 0.008 & 0.007 & 0.008 & 0.008 & 0.01 \\ \hline SU30 & Mean & 0.725 & 0.726 & 0.785 & **0.778** & 0.647 \\ \hline  & SD & 0.01 & 0.009 & 0.009 & 0.008 & 0.016 \\ \hline SU50 & Mean & 0.722 & 0.73 & 0.783 & **0.779** & 0.676 \\ \hline  & SD & 0.009 & 0.009 & 0.009 & 0.008 & 0.013 \\ \hline BW & Mean & 0.699 & 0.71 & 0.757 & 0.735 & 0.68 \\ \hline  & SD & 0.011 & 0.012 & 0.012 & 0.008 & 0.012 \\ \hline CU & Mean & 0.631 & 0.607 & 0.638 & 0.735 & 0.644 \\ \hline  & SD & 0.015 & 0.012 & 0.013 & 0.011 & 0.028 \\ \hline D1U & Mean & **0.76** & **0.774** & **0.806** & 0.77 & **0.739** \\ \hline  & SD & 0.008 & 0.007 & 0.008 & 0.008 & 0.01 \\ \hline D2U & Mean & **0.759** & **0.77** & **0.802** & 0.766 & **0.735** \\ \hline  & SD & 0.008 & 0.006 & 0.008 & 0.009 & 0.012 \\ \hline D4U & Mean & **0.757** & **0.768** & 0.795 & 0.759 & 0.706 \\ \hline  & SD & 0.007 & 0.006 & 0.008 & 0.009 & 0.007 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Mean value and standard deviation (SD) of AUC. The top three strategies per classifier are shown in red text. Figure 2: Box-plots of AUC values for strategies UN, U50, CU, D1U, D2U, D4U and all classifiers. We notice that the best strategy for each classifier, with the exception of Naive Bayesian, is the Randomized Undersampling with the balanced distribution (U50). Also, the classifier with the highest AUC value is the Multilayer Perceptron with back-propagation Neural Network. Regarding the Naive Bayesian classifier, all strategies perform about equally and it seems that only the combination of the SMOTE Oversampling and Randomized Undersampling strategies (SU30, SU50) slightly improve the AUC metric. For the k-Nearest Neighbor and RIPPER classifiers, the U30, U50, SU30 and SU50 strategies significantly improve the performance on AUC, and, especially, the U50 strategy. For the Decision Tree C4.5, only the U50 strategy performs slightly better than the Natural one (UN), and, for the Multilayer Perceptron, the U50 strategy performs better than the Natural one (UN) and the U30 strategy slightly better. The balanced distribution in both the between and within-classes (BW) strategy gave the worst results on AUC with the exception of the RIPPER classifier. Likewise, Figure 2, depicts the box-plots of the corresponding AUC values for the proposed four strategies (CU, D1U, D2U, D4U), and, additionally, the UN and U50 strategies for comparison. The U50 strategy was chosen because of its performance shown in Table 7 and Figure 1. The white box-plots correspond to the UN strategy, the light gray box-plots to the U50 strategy, the dark gray box-plots to the CU strategy, the white box-plots with a pattern of black dots to the D1U strategy, the light gray box-plots with a pattern of black dots to the D2U strategy, and the dark gray box-plots with a pattern of black dots to the D4U strategy. In both Figure 2 and Table 7 that highlights the top three performing strategies per classifier, we notice that the strategies with the temporal distance restriction of each minority example from the closer majority one (D1U, D2U and D4U) perform better than the UN strategy on all classifiers with the exception of the Naive Bayesian classifier. In addition, they perform better than the U50 strategy in the case of the Decision Tree C4.5 and the k-Nearest Neighbor classifiers. Regarding the Multi-layer Perceptron, Naive Bayesian and RIPPER classifiers, the D1U strategy performs about equally to or slightly better than the U50 strategy, while it performs better than the D4U strategy.