## 4 Results and Discussion
### Experimental Dataset and Setup
#### 4.1.2 Experimental Setup  
In this study, WELAD is defined as the experimental group, and the AdaBoost, Bagging, DECORATE, ERT, GBDT, and RF algorithms are listed as the control group. The performance of the WELAD is verified through experiments. The WELAD is implemented via the Python language, due to the decision tree classifier in the machine learning toolkit scikit-learn ([[https://scikit-learn.org/stable/](https://scikit-learn.org/stable/)]([https://scikit-learn.org/stable/](https://scikit-learn.org/stable/))) uses classification and regression trees (CART) as default classification algorithm, all algorithms exception the DECORATE in the control group, the base classifier also use CART. Since scikit-learn  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset ID** & **Dataset Name** & **Samples** & **Features** & **Classes** & **Computational Complexity** \\ \hline
1 & abalone & 4177 & 8 & 5 & \(3.57\times 10^{10}\) \\
2 & australian & 690 & 14 & 2 & \(1.09\times 10^{11}\) \\
3 & balance-scale & 625 & 4 & 3 & \(2.50\times 10^{7}\) \\
4 & breast\_cancer-wis & 699 & 9 & 2 & \(2.25\times 10^{9}\) \\
5 & breast\_cancer & 286 & 9 & 2 & \(3.77\times 10^{8}\) \\
6 & cervical\_cancer & 858 & 32 & 2 & \(1.01\times 10^{17}\) \\
7 & eleveland & 303 & 13 & 5 & \(9.78\times 10^{9}\) \\
8 & crowdsourced-mapping & 10,545 & 28 & 6 & \(8.36\times 10^{17}\) \\
9 & dermatology & 366 & 34 & 6 & \(7.82\times 10^{16}\) \\
10 & diabetes & 768 & 8 & 2 & \(1.21\times 10^{9}\) \\
11 & drug\_consumption & 1885 & 12 & 7 & \(1.75\times 10^{11}\) \\
12 & firm-teacher & 10,796 & 16 & 4 & \(1.22\times 10^{14}\) \\
13 & flags & 194 & 29 & 8 & \(5.86\times 10^{14}\) \\
14 & frogs\_mfccs & 7195 & 22 & 10 & \(4.78\times 10^{15}\) \\
15 & haberman & 306 & 3 & 2 & \(2.25\times 10^{6}\) \\
16 & heart-disease-h & 294 & 13 & 2 & \(9.21\times 10^{9}\) \\
17 & heart-stalog & 270 & 13 & 2 & \(7.76\times 10^{9}\) \\
18 & hepatitis & 155 & 19 & 2 & \(2.39\times 10^{11}\) \\
19 & indian-liver & 583 & 10 & 2 & \(3.48\times 10^{9}\) \\
20 & ionosphere & 351 & 34 & 2 & \(7.20\times 10^{16}\) \\
21 & lymph & 148 & 18 & 4 & \(1.03\times 10^{11}\) \\
22 & mammographic & 961 & 5 & 2 & \(1.48\times 10^{8}\) \\
23 & primary-tumor & 339 & 17 & 2 & \(2.56\times 10^{11}\) \\
24 & segment & 2310 & 19 & 7 & \(5.32\times 10^{13}\) \\
25 & seismic-bumps & 2584 & 18 & 2 & \(3.15\times 10^{13}\) \\
26 & sonar & 208 & 60 & 2 & \(2.99\times 10^{24}\) \\
27 & transfusion & 748 & 4 & 2 & \(3.58\times 10^{7}\) \\
28 & vehicle & 846 & 18 & 4 & \(3.38\times 10^{12}\) \\
29 & waveform & 5000 & 40 & 3 & \(1.10\times 10^{12}\) \\
30 & wholesale & 440 & 7 & 3 & \(1.73\times 10^{8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Information on the UCI datasets used in the experiment.  
does not integrate the DECORATE algorithm, we use the WEKA machine learning toolkit ([[https://www.cs.waikato.ac.nz/ml/weka/](https://www.cs.waikato.ac.nz/ml/weka/)]([https://www.cs.waikato.ac.nz/ml/weka/](https://www.cs.waikato.ac.nz/ml/weka/))) instead, and its base learner uses J48 decision tree.  
The parameter values in the two stages of the WELAD are determined by the grid search method, the bounds of \(\lambda\) ranges from 0.1 to 0.9 step 0.1, \(M\in\{3,5,7\}\), \(SS\in\{20,30,50\}\), and the final choice is \(M=3,\lambda=0.4,SS=30,T_{max}=50\). The number \(L\) of base classifier trees of the experimental group and the control group is uniformly set to 30. For the selection of the training dataset splitting parameter \(K\), according to the idea of majority voting ensemble, a larger \(K\) value will improve accuracy, but for a dataset with a small number of samples, a larger \(K\) value will result in too few samples for training, leading to increase individual errors. In preliminary experiments, we tried to use \(K\in\{5,7,9\}\) and finally chose K = 7.