Neural network work better with small dataset than largest one ?

Hi,i create neural network using nprtool at the begining i used input matrix with 9*981 but i got accuracy in the confusion matrix of 65% then i reduced the samples and i used input matrix with 9*102 and i got accuracy of 94.1% . So is this possible and correct ? and i want to know what's the reason for that.

Thanks

Answer 1

[Jeong_evolution]

If the Input parameter in historical dataset(9*102) is highly correlated(important) with the target, it is possible. And I think historical dataset(9*981) is increased, but it seems to be decreases in correlation or Importance to the target.

[afef]

you mean by " the Input parameter" the samples in the input ? sorry but i don't understand what's historical dataset?

Thanks .

[Jeong_evolution]

Input parameter = Input

target = output

historical dataset = Input+Output(=all dataset)

If you let me know the characteristic of dataset, I will let you know as far as I know.

[afef]

I have some statistical feature extracted from EEG signal to detect epileptic seizure and this is a part of the input and target that i used

Answer 2

[Jeong_evolution]

Add, you have to select Input parameters that is more related with target before using NN.

Answer 3

[Greg Heath]

With respect to the original question:

You really cannot deduce anything worthwhile about performance on the N = 981 dataset by using one subset of n = 102. Also, it is not clear if the 102 are all training data or are divided into trn/val/tst subsets.

A more rigorous approach would be to use m-fold cross validation which uses data RANDOMLY divided into m subsets of size M ~= 981/m. This can be repeated as many times as you want because all of the data is randomly distributed. In particular you can optimize m and separate the 3 trn/val/tst performances.

Note that this is different from traditional stratified m-fold crossval where each point is only in one of the m subsets. However, it is MUCH easier to implement and can be repeated as many times as needed to reduce prediction uncertainties.

Hope this helps.

Thank you for formally accepting my answer

Greg

[afef]

I used at first a dataset with N= 981 and because i didn't get a good accuracy so i tried a small dataset with N= 102 to see if the performance is better . Concerning the m-fold cross validation how could i do it please?