# Interclass Interference Suppression in Multi-Class Problems
## 6 Conclusions  
The generalization ability of classifiers is a crucial problem in pattern recognition. In multi-classifiers, there is a special phenomenon that essential features with the ability to discriminate a certain class from others are often deleted in feature selection. This phenomenon seriously decreases the classification accuracy of multi-classifiers. To address this problem, this paper called this phenomenon interclass inference, analyzed its reasons in detail and summarized three interclass inference suppression methods including all-features-based method, the one-class classifier-based method and the binary-classifier-based method. By comparing the three methods in 14 UCI datasets through 10-fold cross-validation, the following conclusions can be drawn.  
Firstly, the concept of interclass inference is proposed and the essence of interclass inference is revealed. The deletion of essential features leads to interclass inference.  
Secondly, the three methods can all improve the classification accuracy to some extent when they are compared with the conventional algorithm. This shows that suppressing the interclass inference is an effective method to improve the generalization ability of  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**Binary SVM (OVO)**} & \multicolumn{3}{c}{**One-Class SVM**} \\ \cline{2-7}  & **C = 1** & **C = 100** & **C = 1000** & **v = 0.001** & **v = 0.01** & **v = 0.3** \\ \hline zoo & 0.9209 & 0.9609 & 0.9609 & 0.9418 & 0.8718 & 0.8218 \\ lymphography & 0.7852 & 0.8457 & 0.8457 & 0.7233 & 0.7100 & 0.7171 \\ wine & 0.4611 & 0.4892 & 0.4892 & 0.5062 & 0.5229 & 0.5173 \\ flags & 0.3766 & 0.3768 & 0.3768 & 0.2226 & 0.2637 & 0.2639 \\ autos & 0.3469 & 0.3469 & 0.3469 & 0.0433 & 0.1705 & 0.0690 \\ machine & 0.6031 & 0.6031 & 0.6031 & 0.1533 & 0.1629 & 0.1819 \\ images & 0.3048 & 0.3190 & 0.3190 & 0.2905 & 0.2952 & 0.2952 \\ glass & 0.6675 & 0.7000 & 0.6855 & 0.5461 & 0.5506 & 0.5240 \\ audiology & 0.5045 & 0.8198 & 0.8198 & 0.3974 & 0.4334 & 0.4557 \\ heart & 0.5412 & 0.5412 & 0.5412 & 0.1218 & 0.1218 & 0.1218 \\ solar & 0.8855 & 0.8640 & 0.8483 & 0.7925 & 0.8139 & 0.6469 \\ soybean & 0.9398 & 0.9428 & 0.9355 & 0.8536 & 0.8653 & 0.8404 \\ vehicle & 0.2895 & 0.2989 & 0.2989 & 0.3002 & 0.3013 & 0.3013 \\ anneal & 0.9254 & 0.9599 & 0.9599 & 0.8508 & 0.8530 & 0.8408 \\ Mean & 0.6109 & 0.6477 & 0.6451 & 0.4817 & 0.4955 & 0.4712 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison between classification accuracies obtained by binary SVM and one-class SVM.  
multi-classifiers. The binary classifier-based method can suppress the interclass inference best and obtain the best generalization ability.  
Thirdly, this paper compares the suppression effects of two binary classifier-based methods, including one-versus-one (OVO) and one-versus-all (OVA) on interclass inference. OVO method obtains better classification accuracy than the OVA method. Thus, it is the best method to suppress the interclass inference. By studying the interclass inference and its suppression methods, the generalization ability of multi-classifiers is significantly improved.  
Conceptualization, M.B. and J.L.; writing, N.J.; methodology, M.B., X.L., Y.W. and R.C.; supervision, D.Y. and J.L. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China under Grant No. 51976042, National Science and Technology Major Project of China under Grant No. 2017-I-0007-0008, National Science and Technology Major Project of China under Grant No. 2017-V-0005-0055, National Key R&D Program of China No. 2017 YFB0902100 and Science and Technology on Thermal Energy and Power Laboratory Foundation No. TPL2017 CA010.  
Not applicable.  
The authors would like to thank the anonymous reviewers for their valuable suggestions to refine this work.  
The authors declare no conflict of interest.