# Interclass Interference Suppression in Multi-Class Problems
## 5 Experiments
### Comparison Among Different Interclass Interference Suppression Methods
Literature [60] points out the computational burden of the two methods. For a multi-class problem with \(M\) classes, OVO requires \(M(M-1)/2\) base binary classifiers and the computational complexity can be regarded as \(O(M^{2})\). By contrast, OVA requires \(M\) base binary classifiers, and the computational complexity can be regarded as \(O(M)\). Although the OVO-based binary classification method costs more time, OVO can obtain better classification accuracy and thus better interclass inference suppression performance than the OVA method and is more suitable for the case where users require high classification accuracy. \begin{table} \end{table} 
Table 2: Datasets used for experiments. Next, we will analyze the reasons for the performance improvement through some basic evaluation indexes of classifiers. Tables 4 and 5 give the number of selected features, rule number, rule length and rule support coefficient of various methods. From Tables 4 and 5, we can obtain the following results:  
1. The number of attributes and rules obtained by the OVO interclass interference suppression method is significantly smaller than that of the conventional algorithm. Meanwhile, the rule length is shorter, and the support coefficient is larger. Obviously, OVO method can reflect the intrinsic characteristics better and obtain better generalization ability. 2. Compared with the OVO approach, the above-mentioned indexes obtained by the OVA approach are similar to the conventional multi-classifiers, which explains why the performance improvement is not obvious to some extent. In addition, although the negative-classes-based classification obtains larger support coefficients and shorter rules than the positive-classes-based classification, its classification accuracy is not ideal since it is an indirect decision-making strategy. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**Numbers of Attributes**} & \multicolumn{3}{c}{**Numbers of Rules**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Conventional**} & \multirow{2}{*}{**OVA**} & \multirow{2}{*}{**OVO**} & \multicolumn{2}{c}{**Convention**} & \multicolumn{2}{c}{**OVA**} & \multirow{2}{*}{**OVO**} \\  & **Algorithm** & & & & **P** & **N** & \\ \hline zoo & 4.9000 & 2.4571 & 1.0857 & 12.2000 & 10.0000 & 20.7000 & 7.4500 \\ lymphography & 0.0000 & 3.7000 & 2.1000 & 34.9000 & 33.3500 & 39.2500 & 15.7333 \\ wine & 4.0000 & 3.5667 & 2.5000 & 13.5000 & 12.5000 & 16.5000 & 9.1500 \\ flags & 8.8000 & 5.1625 & 2.7571 & 73.8000 & 76.2500 & 144.2500 & 38.9857 \\ autos & 9.2000 & 5.5667 & 2.9600 & 51.1000 & 52.3500 & 82.9500 & 28.6400 \\ machine & 6.7000 & 3.7125 & 1.7250 & 37.0000 & 35.0000 & 69.1000 & 17.7286 \\ images & 6.3000 & 3.2143 & 1.7048 & 28.3000 & 27.6500 & 53.6500 & 16.2500 \\ glass & 6.8000 & 5.1667 & 3.0467 & 32.4000 & 29.9000 & 50.6000 & 18.5400 \\ audiology & 13.3000 & 2.9716 & 1.1023 & 58.1000 & 55.3000 & 122.4000 & 28.7445 \\ heart & 9.8000 & 9.1800 & 8.0100 & 98.2000 & 96.3000 & 135.5000 & 59.8000 \\ solar & 9.0000 & 7.9000 & 6.5667 & 52.1000 & 52.1000 & 63.4000 & 37.5500 \\ soybean & 11.3000 & 3.0421 & 11.234 & 115.7000 & 91.0500 & 151.1500 & 28.5278 \\ vehicle & 14.2000 & 11.5250 & 8.8500 & 181.5000 & 181.9000 & 207.8000 & 91.6333 \\ anneal & 3.0000 & 1.2000 & 1.0000 & 7.0000 & 5.0000 & 8.0000 & 5.5250 \\ Mean & 8.0929 & 4.8832 & 3.1808 & 56.8429 & 54.1893 & 83.2321 & 28.8756 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Numbers of attributes and rules obtained by different methods.