# Interclass Interference Suppression in Multi-Class Problems
## 5 Experiments
### Comparison Among Different Interclass Interference Suppression Methods  
In this section, this paper compares the OVA method and the OVO method with conventional multi-classifiers. This paper uses the algorithm in the literature [51] for feature selection and to classify through LEM2 algorithm [59], a common-used rule-based classification algorithm. For the conventional algorithm, feature selection is made once only. For the OVA and OVO methods, we need to construct \(M\) and \(M(M-1)/2\) classifiers, respectively, and we need to make feature selection for each constructed classifier. The classification accuracy obtained from the various methods is given in Table 3. OVA_V, OVA_C, OVA_PS and OVA_NS represent the strategies of voting based on classification results, voting based on certain coefficients of rule, voting based on the support coefficient of positive classes and voting based on the support coefficient of negative classes for the OVA method, respectively. Meanwhile, OVO_V, OVO_C and OVO_S represent the strategies of voting based on classification results, voting based on certain coefficients of rule and voting  
Figure 1: Store Structure of Consumed Score for the M-Class Problem. based on the support coefficient for the OVO method, respectively. This paper performs 10-fold cross-validation to select the parameters of these algorithms and compares the interclass inference suppression effects. Specifically, the data are randomly divided into ten equal parts. Each unique part is selected as the test set, and the other nine parts are used as the training set. Thus, ten experiments are performed in total. The classification accuracy of 10-fold cross-validation is the mean value of the ten experiments. Corresponding 10-fold cross-validation classification accuracies of these methods are shown in Table 3. From Table 3, the following phenomena can be observed. \begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Size**} & \multicolumn{2}{c}{**Conditional**} & \multirow{2}{*}{
\begin{tabular}{c} **Number** \\ **of Classes** \\ \end{tabular} } \\  & & & & **Attribute** \\  & & & **Number** & **of Classes** \\ \hline
1 & zoo & 101 & 16 & 7 \\
2 & lymphography & 148 & 18 & 4 \\
3 & wine & 178 & 13 & 3 \\
4 & flags & 194 & 28 & 8 \\
5 & autos & 205 & 23 & 6 \\
6 & machine & 209 & 7 & 8 \\
7 & images & 210 & 19 & 7 \\
8 & glass & 214 & 9 & 6 \\
9 & audiology & 226 & 69 & 24 \\
10 & heart & 303 & 13 & 5 \\
11 & solar & 323 & 10 & 3 \\
12 & soybean & 683 & 35 & 19 \\
13 & vehicle & 846 & 18 & 4 \\
14 & anneal & 898 & 38 & 5 \\ \hline \hline \end{tabular}   
* Based on the OVA and OVO methods, the classification accuracy is significantly improved compared with the conventional algorithm. This shows that the binary-classifier-based method can effectively suppress the interclass interference in multi-classifiers. * The OVO method obtains the best classification accuracy among OVO, OVA and conventional algorithms. Compared with conventional algorithms, the OVA method can also improve the classification accuracy, but the improvement is not obvious. This shows that the OVO method can suppress the interclass inference better than the OVA method. The reason for this is that the OVA method usually causes the class-imbalanced problem in classification. * In all kinds of decision strategies, the strategy of voting based on classification results obtains the optimal accuracy, and the strategy of voting based on certain coefficient of rules obtains the suboptimal accuracy. The strategy of voting based on the support coefficient of rules is the worst. For the OVA method, classification based on negative classes obtains a worse classification accuracy than that based on positive classes. This shows that voting based on classification results can be chosen as the optimal collaborative decision strategy. * The OVO-based binary classification method requires building more classifiers than the OVA-based binary classification method and thus usually costs more computational time.