I have got some data partially labelled. Therefore, I would like to apply semi-supervised learning for this dataset. Basically, I trained the Support Vector Machine (SVM) using the data with labels and build the classifier and tune the SVM parameters. Then I do the prediction for the unlabelled data. Here I have got some questions:
- After I have done the training, I have obtained a training accuracy of 96.85%, and the confusion matrix is
Now we can see that the training have got some mis-classification. And should I do the prediction directly now or should I remove the mis-classified cases and then do the prediction for the unlabelled data? By remove the mis-classified data, and then do the training again I can get 100% training accuracy.
- If I don't need to remove the mis-classified data during the training. What are the benefits of this? I think by including the mis-classified cases can increase the variability of the model, am I right?