Neural network with small data set

Hey there,

first of all, I am just a beginner starting with ANN's.

I am trying to make predictions with a simple Neural Network out of very small data sets. For example I got 3 inputs at a sample size of 24 to make predictions for 1 output.

Now my questions: Do you think it is feasible to get adequate results out of such a small sample size? What can I do to get more accuracy?

What can you recommend regarding the amount of layers and neurons?

Thanks for your help!

Answer 1

[Siddharth Solanki]

As per my understanding you are trying to achieve better accuracy with a very small dataset and would like to know ways of improving the accuracy and some suggestions on architecture.

You can try to augment your dataset to increase the effective size of your dataset. Refer to this link for more details on image input. If you have audio input dataset then you can refer to this link.

You can learn more about basics of neural net architecture from this link.

You can also try transfer learning with pretrained nets which might suit your purpose if you have an image dataset - link.

If the dataset allows for using a ML algorithm for solving the problem, then it might be a good way to go about it for a small dataset. You may refer to this link for basics.

[Lukas Schmitt]

Thanks a lot for your answer.

My problem is just a basic regression problem. My inputs are some integer values (properties from defects in metallic specimens, like defect size, position and so on). Now I am not sure how to separate this small amount of data into training and validation sets and what validation method I should use.

Furthermore, I was thinking about data augmentation too, but I don't know how I should do this.