# Interclass Interference Suppression in Multi-Class Problems
## 5 Experiments
### Comparison Among the One-Class Classifier and Binary Classifier-Based Approach  
The method based on one-class classifiers can completely eliminate the class interference existing in multi-classifiers. However, a one-class classifier only uses the information of the target class in the definition of the classification boundary. Many studies show that \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Classification Accuracy**} & \multicolumn{2}{c}{**Number of Rules**} & \multicolumn{2}{c}{**Rule Length**} & \multicolumn{2}{c}{**Support Coefficient**} \\ \cline{2-9}  & **CV\_A** & **OVO\_VA** & **CV\_A** & **OVO\_VA** & **CV\_A** & **OVO\_VA** & **CV\_A** & **OVO\_VA** \\ \hline zoo & 0.9518 & 0.9518 & 9.3000 & 7.3500 & 2.2239 & 1.0544 & 0.1110 & 0.4798 \\ lymphography & 0.8319 & 0.8452 & 25.6000 & 12.9000 & 2.9551 & 2.2118 & 0.0744 & 0.2199 \\ wine & 0.9497 & 0.9552 & 10.1000 & 8.1500 & 2.1206 & 1.6022 & 0.1961 & 0.3247 \\ flags & 0.6187 & 0.6134 & 57.1000 & 31.3429 & 3.3845 & 1.9894 & 0.0266 & 0.1994 \\ autos & 0.7640 & 0.7933 & 48.2000 & 23.8800 & 2.8919 & 1.8957 & 0.0285 & 0.1761 \\ machine & 0.6505 & 0.6743 & 37.2000 & 16.7000 & 2.8934 & 1.7571 & 0.0335 & 0.2673 \\ images & 0.8857 & 0.8857 & 25.0000 & 13.5167 & 2.5977 & 1.4600 & 0.0488 & 0.2854 \\ glass & 0.6955 & 0.6677 & 32.3000 & 18.0200 & 3.1229 & 1.9913 & 0.0336 & 0.1792 \\ audiology & 0.7972 & 0.8241 & 40.7000 & 24.6133 & 3.6348 & 1.1128 & 0.0362 & 0.4837 \\ heart & 0.5216 & 0.5283 & 98.8000 & 58.6000 & 4.8474 & 3.5952 & 0.0152 & 0.0731 \\ solar & 0.8580 & 0.8672 & 51.6000 & 36.3500 & 3.2850 & 2.9984 & 0.0277 & 0.0531 \\ soybean & 0.9165 & 0.9370 & 61.7000 & 22.6667 & 4.0635 & 1.2661 & 0.0207 & 0.4276 \\ vehicle & 0.6760 & 0.7151 & 177.2 & 86.2667 & 4.4825 & 3.6881 & 0.0092 & 0.0381 \\ anneal & 1.0000 & 1.0000 & 5.0000 & 5.0000 & 1.2000 & 1.0000 & 0.2000 & 0.5000 \\ Mean & 0.7941 & 0.8042 & 48.5571 & 26.0969 & 3.1217 & 1.9730 & 0.0615 & 0.2648 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification accuracy obtained by the method based on all features. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Conventional**} & \multirow{2}{*}{**CV\_A**} & \multirow{2}{*}{**OVO\_V**} \\  & **Algorithm** & & & \\ \hline zoo & 4.9000 & 16 & 8.7000 \\ lymphography & 6.0000 & 18 & 8.1000 \\ wine & 4.0000 & 13 & 5.6000 \\ flags & 8.8000 & 28 & 14.9000 \\ autos & 9.2000 & 23 & 12.5000 \\ machine & 6.7000 & 7 & 7.0000 \\ images & 6.3000 & 19 & 11.4000 \\ glass & 6.8000 & 9 & 7.0000 \\ audiology & 13.3000 & 69 & 23.8000 \\ heart & 9.8000 & 13 & 10.0000 \\ solar & 9.0000 & 10 & 8.9000 \\ soybean & 11.3000 & 35 & 24.9000 \\ vehicle & 14.2000 & 18 & 16.1000 \\ anneal & 3.0000 & 38 & 4.9000 \\ Mean & 8.0929 & 22.5714 & 11.7000 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Numbers of attributes obtained by the method based on all features and other methods. the performance of one-class classifiers is usually worse than that of multi-classifiers. In order to confirm that the OVO method is the best choice to suppress interclass interference, we used a binary support vector machine (SVM) based on the OVO method and one-class SVM [61] to carry out a comparison experiment. In the experiment, we did not carry out feature selection. Radial basis function was used for binary SVM and one-class SVM. We first compared the classification accuracy of binary and one-class SVM under different parameters to determine their optimal parameters and obtained their best classification accuracy. Then, the best classification accuracy of the two methods was compared to evaluate the performance of binary and one-class SVM. Results are shown in Table 8.  
From Table 8, we can draw the following conclusions. When we take C = 100 in the experiment, SVM based on the OVO method acquires the best performance. When we take v = 0.01, one-class SVM obtains the better performance.