Timeline for How to deal with class imbalance problem in natural language processing?
Current License: CC BY-SA 4.0
4 events
| when toggle format | what | by | license | comment | |
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| Mar 28, 2021 at 18:46 | vote | accept | LGDGODV | ||
| Mar 28, 2021 at 10:38 | comment | added | Erwan | @LGDGODV In your case I think it boils down to a precision/recall tradeoff: based on the performance values that you report, I assume that you have high precision but low recall with strong imbalance and conversely. So you can use the proportion as a parameter to tune the performance depending on the goal of the task, but I don't think there's a way to improve performance for both classes (unless you find a way to make the model better at classifiying the difficult cases, of course). | |
| Mar 28, 2021 at 5:33 | comment | added | LGDGODV | You are right, I tried classifying texts that are semantically distant apart, even the ratio is 1:10, the model still performs very well. But my case is to classify two types of posts that are semantically close to each other, one-class classification performs poorly in this case. Do you think that change a loss function might improve much for my binary classifier? | |
| Mar 27, 2021 at 12:55 | history | answered | Erwan | CC BY-SA 4.0 |