Neural network work better with small dataset than largest one ?
1 Ansicht (letzte 30 Tage)
Ältere Kommentare anzeigen
afef
am 7 Jun. 2017
Kommentiert: afef
am 11 Jun. 2017
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
0 Kommentare
Akzeptierte Antwort
Jeong_evolution
am 7 Jun. 2017
Bearbeitet: Jeong_evolution
am 7 Jun. 2017
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.
3 Kommentare
Jeong_evolution
am 7 Jun. 2017
Bearbeitet: Jeong_evolution
am 7 Jun. 2017
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.
Weitere Antworten (2)
Jeong_evolution
am 7 Jun. 2017
Add, you have to select Input parameters that is more related with target before using NN.
0 Kommentare
Greg Heath
am 10 Jun. 2017
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
Siehe auch
Produkte
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!