2
$\begingroup$

I am trying to use DBSMOTE(Density-Based Synthetic Oversampling TEqnique) to on a data set of short text--tweets to be specific. This will be used to train a classifier model in a multiclass classification model. This will be done at feature level augmentation, using TF-IDF as features. I have read though, that to use SMOTE on NLP, the feature vectors must be reduced in dimension. What is an optimal size of a feature vector to use in a SMOTE family algorithm?

Similar Question: How do you apply SMOTE on text classification?

DBSMOTE Code: https://rdrr.io/cran/smotefamily/man/DBSMOTE.html

DBSMOTE Paper: https://link.springer.com/article/10.1007/s10489-011-0287-y

$\endgroup$
2
  • $\begingroup$ Hey! It'd be great if you added a link to where you read that about SMOTE on NLP. That way, we can read further on the topic and try to help you on that. $\endgroup$
    – jmnavarro
    Commented May 15, 2018 at 22:42
  • $\begingroup$ Thanks for the tip @jmnavarro, I'm very new to using StackExchange $\endgroup$ Commented May 15, 2018 at 23:03

0

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Browse other questions tagged or ask your own question.