I am doing human activity recognition project. I have total of 12 classes. The class distribution look like this:

enter image description here

$\color{red}{If \ you \ watch \ carefully, you \ can \ see \ that \ I \ have \ no \ data \ points \ for \ class \ 11 \ and \ class \ 8.}$ Also, the dataset is highly imbalanced. So, I took minimum data points (in this case 2028) for all of the classes. Now my balanced data look like this:

enter image description here

After doing this it looks like a balance data. $\color{red}{But \ still, \ I \ think \ it \ not, \ because \ I \ have \ zero \ datapoints \ for \ class \ 11 \ and \ class \ 8}$. In my opinion the classes are still imbalance.

I am using CNN model to solve this activity project. My model summary is following:

enter image description here

The main problem is, my model starts overfitting heavily when I train it.

enter image description here

Is it due to my imbalance data( class 8 and 11 has zero data points) or something else?


$\textbf{features:}$ X, Y, Z of mobile accelerometer

$\textbf{frame size:}$ 80

$\textbf{optimizer:}$ Adam, $\textbf{Learning rate:}$ 0.001

$\textbf{Loss:}$ Sparse categorical cross-entropy


1 Answer 1


Your zero-classes just do not exist for the model. There is no information about them in training and test sets. I think the reason for overfitting is that your have very small size of the training set comparing to the number of parameters in your model. If it is easier for a model to remember all training entries - it will just do that. You need to prune your model to force it to make some generalizations about the data.

  • $\begingroup$ Those images are from the original dataset. Before feeding it to the model , I reduce the class by 1 ( class 1 becomes class 0 and so on). Yes, I know in case of overfitting I have to reduce the model complexity. But I don't think it is the case in this problem, $\endgroup$ Jun 18, 2020 at 8:40
  • $\begingroup$ You have about 20k entries in the training data and 500k parameters to learn on them. The proportion is reversed, you'd better have 5-10m entries in the training data to properly train such a model. $\endgroup$ Jun 18, 2020 at 9:24

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