How to study the effect of each data on a deep neural network model?

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I'm working on a training a neural network model using Python and Keras library.

My model test accuracy is very low (60.0%) and I tried a lot to rise it, but I couldn't. I'm using DEAP dataset (total 32 participants) to train the model. The splitting technique that I'm using is a fixed one. It was as the followings:28 participants for training, 2 for validation and 2 for testing.

For the model I'm using is as follows.

  • sequential model
  • Optimizer = Adam
  • With L2_regularizer, Gaussian noise, dropout, and Batch normalization
  • Number of hidden layers = 3
  • Activation = relu
  • Compile loss = categorical_crossentropy
  • initializer = he_normal

Now, I'm using train-test technique (fixed one also) to split the data and I got better results. However, I figured out that some of the participants are affecting the training accuracy in a negative way. Thus, I want to know if there is a way to study the effect of the each data (participant) on the accuracy (performance) of a model?

Best Regards,

keras training-data machine-learning neural-network python
2 Answers

This is, perhaps, more broad an answer than you may like, but I hope it'll be useful nevertheless.

Neural networks are great. I like them. But the vast majority of top-performance, hyper-tuned models are ensembles; use a combination of stats-on-crack techniques, neural networks among them. One of the main reasons for this is that some techniques handle some situations better. In your case, you've run into a situation for which I'd recommend exploring alternative techniques.

In the case of outliers, rigorous value analyses are the first line of defense. You might also consider using principle component analysis or linear discriminant analysis. You could also try to chase them out with density estimation or nearest neighbors. There are many other techniques for handling outliers, and hopefully you'll find the tools I've pointed to easily implemented (with help from their docs); sklearn tends to readily accept data prepared for Keras.

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