Ratings and Rankings -- Using Deep Learning When Class Labels Have A Natural Order

[@nguyenhuuuc2311]

Now ! CORAL mlp model for tabular data is one of my choices besides LGBM and XGBoost. When the talk ended, I saw a few tweets about that. I didn't think It'll be uploaded on TH-cam. Thus, I reached CORAL documentation and played around with it and made a few comparisons with LGBM. It's better than LGBM in that problem (both are tuned).
- Thanks for your uploading so that I can learn and understand the architecture of CORAL/CORN more! 😊

[@SebastianRaschka]

Wow I am glad to hear that it's that useful. Amazing to hear that it also outperforms LGBM on your problem task! :)

[@736939]

Interesting. Did you separated the classes according the proportion of logits ? Like in formula below (for the linear scaling cases)
class_interval = (argmin(logits) + argmax(logist)) / num_classes

[@SebastianRaschka]

Thanks for the comment! Do you mean whether I tried this method for comparison?

[@736939]

@@SebastianRaschka Yes, I mean this or it gives rank inconsistency? Also, is it possible to classify a hierartchical classes by using similar approach? Idea: the class that on the top of another class, intuitively should have higher rank logits.

[@SebastianRaschka]

@@736939 Interesting, what loss function would you use with that though (e.g. considering that argmax/argmin are not differentiable)?

[@SebastianRaschka]

@@736939 I do like the idea with the hierarchical classes. I think that esp with the CORN approach and conditional probabilities, that could be something interesting to build (if I ever find the time)! Thanks for suggesting :)

[@736939]

@@SebastianRaschka No, I mean at the end stage when we produce classifcation, to use proportion between number of classes and the range between the min and max logits. To separate decision onto equal ranges between classes from min to max logit.