# Interclass Interference Suppression in Multi-Class Problems
## 5 Experiments
### Performance of the All-Features-Based Approach  
When dealing with multi-class problems, deletion of essential features leads to interclass interference. If all features are reserved, interclass inference can be eliminated. Thus, this paper evaluates the effect of this method on interclass interference through experiments. Table 6 shows the classification accuracy, number of rules, rule length and the support coefficient of rules obtained by the all-features-based method and the OVO method. In Table 6, CV_A represents the all-features-based methods and OVO_VA represents the method that reserves all features and uses OVO strategy and makes classification by voting based on the classification results of each classifier. By comparing it with Tables 3-5, the following phenomena are observed. 1. By reserving all features, the classification accuracy of multi-class problems is obviously improved. Further comparison shows that all-features-based method obtains fewer rules and a larger support coefficient than the conventional algorithm. This shows that each class can be expressed by its own essential features and that the intrinsic characteristics of data can be reflected better when reserving all features. This can explain the why all-features-based method obtains better classification accuracy to some extent. 2. The OVO_VA method and OVO_V method has few differences in classification accuracy. Thus, reserving all features has little effect on the classification accuracy of OVO method. This is because the OVO method itself can make use of the essential features to express the knowledge and suppress interclass interference. 3. The all-features-based method still obtains worse classification accuracy than the OVO_V method shown in Table 3. Compared with the conventional algorithm and the all-features-based algorithm, the OVO method extracts simpler and clearer classification knowledge and stronger obtained rules. Thus, the OVO method obtains better generalization ability. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**Rule Length**} & \multicolumn{4}{c}{**Rule Support Coefficient**} \\ \cline{2-9}  & **Conventional** & \multicolumn{2}{c}{**OVA**} & \multirow{2}{*}{**OVO**} & \multirow{2}{*}{**Convention**} & \multicolumn{2}{c}{**OVA**} & \multirow{2}{*}{**OVO**} \\  & **Algorithm** & **P** & **N** & & & **P** & **N** \\ \hline zoo & 2.3088 & 2.0904 & 1.1108 & 1.0402 & 0.0823 & 0.1024 & 0.3679 & 0.4726 \\ lymphography & 2.5843 & 2.5264 & 2.3725 & 2.0472 & 0.0457 & 0.0471 & 0.0919 & 0.1438 \\ wine & 2.1845 & 2.1782 & 1.6184 & 1.5169 & 0.1070 & 0.1027 & 0.1755 & 0.2150 \\ flags & 2.8297 & 2.7584 & 1.8370 & 1.6013 & 0.0175 & 0.0155 & 0.0739 & 0.1219 \\ autos & 2.8248 & 2.6671 & 1.9757 & 1.7014 & 0.0246 & 0.0210 & 0.0831 & 0.1202 \\ machine & 2.8934 & 2.9594 & 2.0499 & 1.6984 & 0.0334 & 0.0266 & 0.1263 & 0.2206 \\ images & 2.5950 & 2.5412 & 1.0686 & 1.3716 & 0.0423 & 0.0370 & 0.1373 & 0.2281 \\ glass & 3.1033 & 3.1045 & 2.1525 & 1.9460 & 0.0335 & 0.0314 & 0.1433 & 0.1732 \\ audiology & 3.2776 & 2.9791 & 1.4881 & 1.0709 & 0.0223 & 0.0198 & 0.2522 & 0.4143 \\ heart & 4.8349 & 4.9566 & 3.8769 & 3.5863 & 0.0155 & 0.0125 & 0.0657 & 0.0649 \\ solar & 3.3148 & 3.4920 & 3.0869 & 2.9350 & 0.0272 & 0.0222 & 0.0442 & 0.0503 \\ soybean & 4.1696 & 3.8603 & 2.1925 & 1.2280 & 0.0105 & 0.0112 & 0.1676 & 0.3356 \\ vehicle & 4.4220 & 4.5494 & 3.9427 & 3.6300 & 0.0087 & 0.0069 & 0.0266 & 0.0287 \\ anneal & 1.7143 & 1.2000 & 1.0000 & 1.0000 & 0.1429 & 0.2000 & 0.5888 & 0.4526 \\ Mean & 3.0755 & 2.9902 & 2.1652 & 1.8838 & 0.0438 & 0.0469 & 0.1674 & 0.2173 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Length and support coefficient of rules obtained by different methods. Compared with conventional algorithms, the all-features-based algorithm can effectively suppress interclass interference and achieve better generalization ability in multi-class problems. However, the method also preserves redundant features, leading to more computational cost.