# Exploiting Domain Knowledge to Address Class Imbalance in Meteorological Data Mining
## 5 Experiments and Results
### Algorithm Runs  
To recap, we tested each one of the first nine strategies with 100 training/test datasets (UN, U30, U50, SU30, SU50, BW, CU, D1U and D2U) and the tenth strategy with 10 training/test datasets (D4U), for a total of 910 training/test datasets. These datasets comprised the input to the five classifiers that were run and evaluated using WEKA. The classifiers were the decision tree C4.5 without pruning and Laplace estimate (DT), the k-Nearest Neighbors with k = 5 and Euclidean distance (kNN), the RIP-PER (RIP), the Naive Bayesian (NB), and the Multilayer Perceptron neural network with back-propagation (MP). The last three classifiers were run using the default settings of WEKA. Thus, we performed 4550 runs in the WEKA environment and we present the results in Table 7 and in Figures 1 and 2. Table 7 shows the mean value and the standard deviation of AUC of the 100 or 10 (for D4U) runs for each strategy and classifier. Since it is impossible to plot all the box plots for all strategies and classifiers in a single figure, we decided to use two figures. In the first figure, we compare the strategies commonly used in the literature (2 through 6) against UN (strategy 1 that simply uses the initial unbalanced dataset). In the second figure, we compare the newly proposed strategies (7 through 10) against UN and the best strategy of the first figure. Thus, Figure 1 depicts the box-plots of the corresponding AUC values for the first six strategies. The white box-plots correspond to the UN strategy, the light gray box-plots to the U30 strategy, the light gray box-plots with a pattern of black dots to the U50 strategy, the dark gray box-plots with a pattern of \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Precipitation ‘Yes’**} & \multicolumn{3}{c}{**Precipitation ‘No’**} \\ \cline{2-9}  & **Rain/Drizzle** & **Snow/Sleet** & **Thunder** & **Total ‘Yes’** & **Fog** & **Fair/Cloudy** & **Total ‘No’** & **Total** \\  & **11.66\%** & **0.89\%** & **3.55\%** & **16.10\%** & **2.27\%** & **81.62\%** & **83.90\%** & **100.00\%** \\ \hline Initial & 7154 & 547 & 2181 & 9882 & 1395 & 50,087 & 51,482 & 61,364 \\ Groups & 6438 & 493 & 1963 & 8894 & 1256 & 45,078 & 46,334 & 55,228 \\ Folds & 715 & 55 & 218 & 988 & 140 & 5008 & 5148 & 6136 \\ \hline \multicolumn{9}{l}{**Strategy**} \\ \hline UN & 2069 & 171 & 639 & 2879 & 443 & 14,466 & 14,909 & 17,788 \\ U30 & 3863 & 296 & 1177 & 5336 & 362 & 12,090 & 12,452 & 17,788 \\ U50 & 6438 & 493 & 1963 & 8894 & 250 & 8644 & 8894 & 17,788 \\ SU30 & 3863 & 296 & 1177 & 5336 & 362 & 12,090 & 12,452 & 17,788 \\ SU50 & 6438 & 493 & 1963 & 8894 & 250 & 8644 & 8894 & 17,788 \\ BW & 2965 & 2964 & 2965 & 8894 & 4447 & 4447 & 8894 & 17,788 \\ CU & 6438 & 493 & 1963 & 8894 & 250 & 8644 & 8894 & 17,788 \\ D1U & 6438 & 493 & 1963 & 8894 & 250 & 8644 & 8894 & 17,788 \\ D2U & 6438 & 493 & 1963 & 8894 & 193 & 8538 & 8731 & 17,625 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The natural distribution and the number of examples within the precipitation class variable of the training datasets generated by the various sampling strategies. Figure 1: Box-plots of AUC values for strategies UN, U30, U50, SU30, SU50, BW and all classifiers. black dots to the SU50 strategy and the white box-plots with a pattern of black dots to the BW strategy.