## 4 Experiments
### Datasets
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{6}{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 & 0.804 & **0.857** & 0.786 & 0.640 & 0.528 & 0.603 & 0.673 \\ Breast w & **0.988** & **0.988** & 0.947 & 0.951 & 0.936 & 0.936 & 0.914 \\ Congressional voting & **0.981** & 0.973 & 0.881 & 0.920 & 0.937 & 0.953 & 0.953 \\ Ecoli & 0.850 & 0.921 & 0.000 & **0.978** & 0.973 & 0.948 & 0.969 \\ Hayes-Roth & **0.887** & 0.873 & 0.718 & 0.819 & 0.777 & 0.823 & 0.857 \\ Mammographic mask & 0.820 & 0.799 & **0.861** & 0.816 & 0.801 & 0.791 & 0.818 \\ Monk’s problems 2 & 0.675 & 0.857 & 0.807 & **1.000** & 0.485 & 0.583 & 0.000 \\ Pima india diabetes & 0.727 & 0.780 & 0.750 & 0.762 & 0.759 & 0.763 & **0.798** \\ Prm synth & 0.872 & 0.767 & 0.836 & **0.899** & 0.799 & 0.789 & 0.847 \\ Sonar & 0.663 & 0.753 & **0.850** & 0.782 & 0.807 & 0.846 & 0.768 \\ Soybean small & **1.000** & **1.000** & **1.000** & 0.993 & **1.000** & **1.000** & **1.000** \\ SPECTheart & **0.800** & 0.750 & 0.627 & 0.649 & 0.707 & 0.707 & 0.627 \\ Tae & **0.795** & 0.789 & 0.737 & 0.739 & 0.735 & 0.724 & 0.768 \\ Thyroid disease & 0.960 & 0.960 & 0.809 & **0.975** & 0.935 & 0.951 & 0.971 \\ Wisconsin & **0.983** & 0.983 & 0.951 & 0.959 & 0.936 & 0.936 & 0.921 \\ \hline
**Average** & 0.854 & 0.870 & 0.771 & **0.859** & 0.808 & 0.824 & 0.791 \\ \hline
**Improvement** & \(-0.016\) & 0.083 & \(-0.005\) & 0.046 & 0.030 & 0.063 & 0.040 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The specificity of the proposed CAV compared with CAC, SVM, k-NN, DNN-1, DNN-2 and naive Bayes (the bold number indicates the maximum value). Figure 8: CAV with other classifiers; accuracy comparison for the test datasets. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{6}{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 & 0.712 & 0.655 & **0.736** & 0.643 & 0.571 & 0.634 & 0.674 \\ Breast w & **0.977** & 0.973 & 0.963 & 0.965 & 0.959 & 0.950 \\ Congressional voting & **0.970** & 0.954 & 0.922 & 0.939 & 0.952 & 0.953 & 0.949 \\ Ecoli & 0.843 & 0.803 & 0.000 & 0.734 & 0.745 & **0.846** & 0.738 \\ Hayes-Roth & **0.841** & 0.827 & 0.657 & 0.776 & 0.691 & 0.748 & 0.788 \\ Mammographic mask & **0.827** & 0.825 & 0.809 & 0.795 & 0.824 & 0.816 & 0.806 \\ Monk’s problems 2 & 0.520 & 0.585 & **0.827** & 0.814 & 0.651 & 0.728 & 0.000 \\ Pima india diabetes & **0.743** & 0.710 & 0.690 & 0.683 & 0.724 & 0.723 & 0.728 \\ Prm synth synth & 0.831 & 0.787 & **0.844** & 0.827 & 0.835 & 0.833 & 0.844 \\ Sonar & 0.737 & 0.740 & **0.874** & 0.807 & 0.802 & 0.812 & 0.690 \\ Soybean small & **1.000** & **1.000** & **1.000** & 0.990 & **1.000** & **1.000** & **1.000** \\ SPECTheart & **0.748** & 0.612 & 0.733 & 0.638 & 0.713 & 0.713 & 0.733 \\ Tae & **0.684** & 0.674 & 0.587 & 0.595 & 0.612 & 0.452 & 0.628 \\ Thyroid disease & 0.943 & 0.942 & 0.808 & **0.967** & 0.948 & 0.931 & 0.956 \\ Wisconsin & 0.977 & 0.974 & 0.966 & 0.970 & 0.962 & 0.962 & 0.953 \\ \hline
**Average** & **0.824** & 0.804 & 0.761 & 0.810 & 0.799 & 0.807 & 0.762 \\ \hline
**Improvement** & \(0.020\) & 0.063 & 0.014 & 0.024 & 0.016 & 0.061 & 0.040 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The G-mean of the proposed CAV compared with CAC, SVM, k-NN, DNN-1, DNN-2, and naive Bayes (the bold number indicates the maximum value). In terms of classification accuracy, Table 4 shows that the proposed methods have the highest classification accuracy for the following datasets: congressional voting, monk problem 2, SPECT heart, mammographic mass, Hayes-Roth, Tae, Breast w, Analcatdata boxing2, soybean (small), and Pima Indians diabetes.