## 9 Results and Discussion  
This article highlights three different aspects of ensemble and statistical classifiers depicted in Tables 5-10 supplemented by the relevant graphical presentation in Figures 2-13, respectively. This section carried out a detailed comparison to highlight the imbalance and overlapping nature of multi-class classification by applying traditional and ensemble approaches followed by some statistical analysis. Here, we applied different ensemble classifiers (GB, RF, and DT) and non-ensemble approaches (KNN, linear, kernel SVM, and NB) on 15 multi-class datasets. Each dataset consists of a different number of samples, features, and classes, i.e., the instances vary in the range of 150-12,960, while the number of features ranges from 4 to 65 and the number of classes varies between 3 and 20. Our results consist of three parts; first, we explore the six different classifiers on 15 multi-class datasets with default parameters and without synthetic overlapping, using confusion matrix values to gain insight into the different performance measures such as _accuracy_, _precision_, _recall_, and _f1 score_ as depicted in Tables 5 and 6. In the second step, the six algorithms, namely \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Dataset** & **Attributes** & **Samples** & **Classes** & **The Ratio of Each Class** \\ \hline IRIS & 4 & 150 & 3 & 0.333, 0.333, 0.333 \\ Glass & 9 & 214 & 6 & 0.3271, 0.0794, 0.0421, 0.3551, 0.1355, 0.0607 \\ HAR & 561 & 10,299 & 6 & (22.94, 77.06) \\ Breast\_Tissue & 9 & 106 & 6 & 0.2075, 0.1981, 0.1320, 0.1415, 0.1509, 0.1698 \\ Bridges & 13 & 107 & 6 & 0.1415, 0.1037, 0.0849, 0.4245, 0.1037, 0.1509 \\ Car & 7 & 1728 & 4 & 0.2228, 0.405, 0.7002, 0.0381 \\ CMC & 10 & 1473 & 3 & 0.4270, 0.2260, 0.3469 \\ Dermatology & 35 & 366 & 6 & 0.306 0.1967, 0.1666, 0.142, 0.1338, 0.0546 \\ Ecoli & 9 & 336 & 8 & 0.4255, 0.2291, 0.0059, 0.009, 0.1041, 0.0595, 0.0148, 0.1547 \\ LED\_Domain & 8 & 500 & 10 & 0.1233, 0.1355, 0.452, 0.4311, 0.1677, 0.013, 0.2033, 0.1576, 0.432, \\ Nersery & 9 & 12,960 & 5 & 0.3333, 0.3291, 0.0001, 0.3120, 0.0253 \\ Page\_Block & 11 & 5473 & 5 & 0.8978, 0.0601, 0.0051, 0.0159, 0.0210 \\ Satimage & 37 & 6430 & 6 & 0.2382, 0.1092, 0.2110, 0.0973, 0.1099, 0.2343 \\  & & & & 0.0234, 0.1332, 0.0644, 0.0292, 0.0292, 0.1346, 0.0644, 0.0292, \\ Soyaben & 36 & 683 & 19 & 0.0204, 0.0219, 0.0292, 0.0292, 0.1332, 0.0117, 0.0292, 0.1288, \\  & & & & 0.0292, 0.0292, 0.0292 \\ U\_Knowledge & 6 & 403 & 5 & 0.2506, 0.3200, 0.327, 0.2382, 0.0645 \\ Vehicle & 19 & 846 & 4 & 0.2576, 0.2505, 0.2565, 0.2352 \\ Volcanoesa & 4 & 3253 & 5 & 0.9077, 0.0209, 0.0178, 0.0264, 0.0271 \\ Wine & 14 & 178 & 3 & 0.3988, 0.3314, 0.2696 \\ WI\_Following & 25 & 5456 & 4 & 0.4041, 0.3843, 0.1513, 0.0601 \\ Opt\_Digits & 65 & 5620 & 19 & 0.1017, 0.1016, 0.101, 0.1007, 0.1, 0.0992, 0.0992, 0.0991, 0.0985 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Dataset with Description. Figure 8: Recall: ensemble versus non-ensemble after hyper-tuning. Figure 6: Accuracy: ensemble versus non-ensemble after hyper-tuning. Figure 7: Precision: ensemble versus non-ensemble after hyper-tuning. Figure 9: F1-Score: ensemble versus non-ensemble after hyper-tuning. In the third part, the existing datasets (the majority class of each dataset) are synthetically overlapped by 20% of the original samples to glorify the impact of hyper-tuning the parameters, as shown in Tables 9 and 10. After examining the results, we come up with these observations: gradient boosting an ensemble approach based on boosting shows remarkable performance in all categories (with and without hyper-tuning parameters for existing and synthetically overlapped datasets) for most of the multi-class imbalanced datasets. GB is an ensemble method, which combines many weak learners to produce  
Figure 11: Precision: Ensemble versus non-ensemble after hyper-tuning and synthetic overlapping. Figure 12: Recall: ensemble versus non-ensemble after hyper-tuning and synthetic overlapping.