## 4 Experiments
### Datasets  
Datasets from the OpenML repository were used, consisting of different types of instances, features, and numbers of dataset classes, as shown in Table 3. The results demonstrate that the datasets form clusters, as shown by Analcatdata boxing2 (Figure 7a), Breast_w dataset (Figure 7b), Prnn synth (Figure 7d), and Mammographic mass dataset (Figure 7h), which clustered with the mean datasets. However, non-conforming data, such as Hayes roth (Figure 7c), Tae (Figure 7e), Wisconsin (Figure 7f), and SPECTheart (Figure 7g), were identified. The scatter plot results reveal ambiguous data, as most of the data from different classes are mixed, and the classification accuracy is lower when dealing with this type of data. \begin{table}
\begin{tabular}{c c c} \hline
**Classifier** & **Parameters** & **Value** \\ \hline CAV & Population & 100 \\  & Probability switch (p) & 0.5 \\  & Power exponent & 0.4 \\  & Sensory modality & 0.03 \\  & Max iteration & 20 \\ \hline CAC & Sample & 100 \\  & Crossover probability & 48 \\  & Mutation probability & 40 \\  & Max iteration & 20 \\ \hline SVM & SVM type & C-SVM \\  & Kernel type & Rbf \\  & Class weight & 1.0 \\ \hline kNN & K (neighbor) & 5 \\  & Weighted vote & Yes \\ \hline DNN-1 & Activation & Rectifier \\  & Hidden layer number & 2 \\  & Hidden layer size & 50 \\ \hline DNN-2 & Activation & Maxout \\  & Hidden layer number & 3 \\  & Hidden layer size & 100 \\ \hline Naive Bayes & Laplace correction & Yes \\ \hline \end{tabular}
\end{table} 
Table 2: Classification method parameters. \begin{table}
\begin{tabular}{l c c c} \hline
**Dataset** & **Class** & **Instances** & **Features** \\ \hline Soybean (small) & 4 & 47 & 35 \\ SPECT heart & 2 & 80 & 23 \\ Analcatdata boxing2 & 2 & 132 & 10 \\ Tae & 3 & 151 & 6 \\ Hayes-Roth & 3 & 160 & 5 \\ Wisconsin & 2 & 194 & 33 \\ Sonar & 2 & 208 & 61 \\ Prnn synth & 2 & 256 & 3 \\ Thyroid Disease & 2 & 306 & 3 \\ Ecoli & 8 & 336 & 8 \\ Congressional Voting & 2 & 435 & 16 \\ Monk Problem 2 & 2 & 601 & 7 \\ Breast w & 2 & 699 & 10 \\ Pima Indians Diabetes & 2 & 768 & 8 \\ Mammographic Mass & 2 & 961 & 6 \\ \hline \end{tabular}
\end{table} 
Table 3: Datasets. Figure 7: Illustration of dataset distribution using the PCA technique. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{5}{c}{**Classification Accuracy (K = 10)**} & \multirow{2}{*}{**Average Improvement**} \\ \cline{2-2} \cline{5-8}  & **CAV** & **CAC** & **SVM** & **kNN** & **DNN-1** & **DNN-2** & **Naïve Bayes** \\ \hline Analcatdata boxing2 & 0.631 & 0.500 & **0.689** & 0.646 & 0.617 & 0.667 & 0.675 \\ Breast w & 0.967 & 0.958 & 0.980 & 0.980 & 0.982 & 0.986 & **0.986** \\ Congressional voting & 0.960 & 0.935 & 0.965 & 0.958 & 0.967 & 0.952 & 0.944 \\ Ecoli & **0.835** & 0.700 & 0.200 & 0.551 & 0.571 & 0.755 & 0.563 \\ Hayes-Roth & **0.798** & 0.783 & 0.601 & 0.735 & 0.615 & 0.679 & 0.742 \\ Mammographic mass & 0.834 & **0.851** & 0.760 & 0.775 & 0.847 & 0.843 & 0.794 \\ Morik’s problems 2 & 0.400 & 0.400 & 0.849 & 0.663 & 0.874 & **0.908** & 0.653 \\ Pima indiv2 diabetes & **0.760** & 0.646 & 0.635 & 0.612 & 0.690 & 0.686 & 0.664 \\ Prm synth & 0.791 & 0.808 & 0.853 & 0.762 & 0.874 & **0.880** & 0.841 \\ Sonar & 0.819 & 0.728 & **0.898** & 0.833 & 0.798 & 0.779 & 0.619 \\ Soybean small & **1.000** & **1.000** & **1.000** & 0.986 & **1.000** & **1.000** & **1.000** \\ Prm synth & 0.790 & 0.500 & **0.857** & 0.628 & 0.718 & 0.718 & **0.857** \\ Tae & **0.589** & 0.576 & 0.469 & 0.479 & 0.511 & 0.282 & 0.513 \\ Thyroid disease & 0.927 & 0.925 & 0.806 & 0.960 & **0.962** & 0.911 & 0.942 \\ Wisconsin & 0.971 & 0.966 & 0.982 & 0.982 & **0.999** & 0.989 & 0.986 \\ \hline
**Average** & 0.799 & 0.752 & 0.769 & 0.770 & 0.801 & **0.802** & 0.785 \\ \hline
**Improvement** & & 0.047 & 0.029 & 0.029 & -0.002 & -0.003 & 0.014 & 0.023 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The recall of the proposed CAV compared with CAC, SVM, k-NN, DNN-1, DNN-2 and naïve Bayes (the bold number indicates the maximum value).