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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.

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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.

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  • $\begingroup$ Dear Vivien, I write a script that works on another dataset well. But in this dataset, the result is as above mentioned. the data in both datasets are the soundtrack and I use same script to feature extraction. I couldn't find the error!! $\endgroup$ Nov 9 '20 at 21:24

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