## 6 Results
### Results from Individual Metrics  
In this section, the performance of all resampling strategies for all quality metrics regarding dataset characteristics (number of attributes, number of instances, imbalance ratio, borderline examples and overlapping between classes) is analyzed. The results of the metrics are represented through radar charts where their values are placed on the radius of the circle and the characteristic under study on the external side of its perimeter. Original and resampled datasets are represented as lines into the circle in different colors. In addition, statistical significance tests were conducted to compare the results and determine the best resampling method for every dataset. Finally, association rule models were developed in order to find out the most suitable resampling method according to dataset characteristics. Accuracy is the first metric to be studied; it gives the percentage of instances correctly classified, but it does not provide information about which is the success rate in each \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline ID & Name of Dataset & \#inst & \#attrib & IR & BL\% & OVL\% & Source \\ \hline
8 & Hill\_Valley\_with\_Noise\_Testing & 606 & 100 & 8.93 & 24 & 55 & UCI \\
9 & Ecoli-0-4-6\_vs\_5 & 203 & 6 & 9.15 & 7 & 49 & KEEL \\
10 & GD15 & 11,000 & 40 & 9.36 & 15 & 44 & Scikit learn \\
11 & Vowel0 & 988 & 13 & 9.97 & 2 & 68 & KEEL \\
12 & Glass-0-1-6\_vs\_2 & 192 & 9 & 10.29 & 18 & 71 & KEEL \\
13 & Glass2 & 214 & 9 & 11.59 & 18 & 71 & KEEL \\
14 & Infection & 4615 & 15 & 13.89 & 14 & 61 & ICU \\
15 & Ecoli4 & 336 & 7 & 15.80 & 3 & 56 & KEEL \\
16 & Page-blocks-1-3-vs-4 & 472 & 10 & 15.85 & 1,7 & 10 & KEEL \\
17 & Abalone-9-18 & 731 & 7 & 16.40 & 9 & 62 & KEEL \\
18 & Glass5 & 214 & 9 & 22.77 & 7 & 60 & KEEL \\
19 & Yeast-2-vs-8 & 482 & 8 & 23.10 & 4 & 71 & KEEL \\
20 & GD3 & 5500 & 30 & 24.70 & 11 & 91 & Scikit learn \\
21 & Yeast4 & 1484 & 8 & 28.10 & 6 & 57 & KEEL \\
22 & Yeast5 & 1484 & 8 & 32.73 & 3 & 48 & KEEL \\
23 & GBC & 1600 & 25 & 38.00 & 7 & 88 & Scikit learn \\
24 & Winelly\_white-3-vs-7 & 900 & 11 & 44 & 4 & 63 & KEEL \\
25 & Winelly\_red-8\_vs\_6-7 & 855 & 11 & 46.5 & 5.7 & 67 & KEEL \\
26 & GD1 & 2500 & 40 & 54.55 & 5.2 & 93 & Scikit learn \\
27 & Winelly\_white-3-9-vs-5 & 1482 & 11 & 58.28 & 3.77 & 60 & KEEL \\
28 & GD4 & 2500 & 13 & 59.00 & 5 & 76 & Scikit learn \\
29 & GD5 & 2000 & 5 & 59.61 & 4.55 & 55 & Scikit learn \\
30 & GD7 & 6000 & 100 & 60.85 & 4.46 & 96 & Scikit learn \\
31 & GD8 & 3500 & 15 & 62.63 & 4.7 & 83 & Scikit learn \\
32 & GD9 & 10,000 & 50 & 62.69 & 4.7 & 94 & Scikit learn \\
33 & Abalone-20-vs-8-9-10 & 1916 & 7 & 72.69 & 2.71 & 58 & KEEL \\
34 & GD10 & 10,100 & 60 & 72.72 & 4 & 95 & Scikit learn \\
35 & Poker-8-vs-6 & 1477 & 10 & 85.88 & 0.94 & 78 & KEEL \\
36 & GD11 & 3000 & 3 & 99.00 & 3.3 & 61 & Scikit learn \\
37 & Abalone19 & 4174 & 7 & 129.44 & 2 & 59 & KEEL \\
38 & GD12 & 6000 & 20 & 156.89 & 2.2 & 90 & Scikit learn \\
39 & GD13 & 5000 & 5 & 165.66 & 1.7 & 66 & Scikit learn \\
40 & GD14 & 10,000 & 60 & 221.22 & 1.43 & 92 & Scikit learn \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._class. Figure 1 shows the accuracy achieved by applying resampling techniques to different datasets and the impact of different data characteristics. The finding that can be appreciated at first sight is the absence or very little improvement achieved with resampling techniques regardless of the number of attributes, number of instances, imbalance ratio, borderline examples and overlapping classes. This fact is not surprising because random forest is an algorithm that produces good results in data imbalance contexts, achieving an overall acceptable accuracy although the accuracy of the minority class may be low. The most remarkable aspect is the drastic decrease of accuracy when using the RUS method. This can be due to the loss of information that the undersampling technique causes. AUC is a metric that may be more useful than accuracy since it considers the rate of true positives and the rate of false positives.