Why multi-class SVM can't detect some classes? I use PCA and multi-class SVM for classifying 4-class problem in the Python environment. But in results, I see some differences in detection rate (Unweighted Accuracy in this problem). For example:

FV  | cl.1  % |   cl.2  %  |   cl.3  % |    cl.4 % |

fv1  | 5.4 % | 47.4 % |  2.6 % |   78.6 % |

fv2 | 2.6 % |  41.6 % |  8.3 % |   78.4 % |

fv3 | 5.9 % |  49.3 % |  1.8 % |   76.5 % |


you can see that SVM couldn't detect classes 1 and 3 as well as classes 2 and 4. I would be appreciated if anybody helps me to solve this issue.
Well, based on how PCA works, and the fact that you're using multi-class SVM, which is a pretty solid family of algorithm, the only possibility i can think of is that the problem comes from your data. It can still depends on the implementation you're using, but i think it come from your data.