I'm using the data analysis software Orange to analyze rows of data with labels $\{H, T,L\}$. $T$ is the neutral state of the system I'm trying to model so data is almost always labeled with $T$. This leads to an extremely high $97$% classification accuracy, that is untrue. Almost no datapoint with an $H, L$ actually gets labeled with an $H, L$ respectively by the neural net. The neural net is doing what's logical: flatten everything to $T$ since that occurs most of the time. How can I repair this situation?

  • There are downsampling and oversampling approaches to mitigate the imbalance. There are also cost-sensitive approaches, but I'm not sure if they can be applied with neural networks. Also consider computing recall metrics, they should be very low. – Gabriel Romon Aug 27 at 8:07
up vote 2 down vote accepted

There are a few ways:

  1. Oversample the under-represented class. issue: Leads to very constrained boundaries (not smooth) around the under-represented class

  2. Weigh the loss coming from under-represented class more than over-represented class. If you think about it, this is mathematically doing the same thing as 1

  3. Use SMOTE (here) (i recommend this). Basically samples additional points randomly from lines joining nearest under-represented classes and increases the dataset size accordingly. scikit contrib implementation of smote

There are other ways but they offer very marginal improvement over SMOTE

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