Neural network with limited datasets
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Hi all,
I am developing back-propagation neural network to classify the incidence of crisis (crisis=1; non-crisis=0) with 15 covariates (a set of macro and economic indicators). I have annual datasets 1970-2012 (42 observations) which I consider it is considerably small for this exercise.
My questions are:
1. Is it okay to proceed for the BP simulation with small datasets and relatively high number of covariates?
2. When I run the simulation, the result keeps changes overtime (In fact, it has similar datasets, training and test data). I just curious why is it happening?
3. Any idea what is the most appropriate classification method to handle small datasets?
Your responses are highly appreciated.
Thanks
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Greg Heath
2014 年 6 月 18 日
編集済み: Greg Heath
2014 年 6 月 18 日
[ I N ] = size(inputs) % [ 15 42 ]
[ O N ] = size(targets) % [ 1 42 ]
Ntrn = N -2*round(0.15*N) % 30 default (6 val and 6 test)
Ntrneq = N*O % 30 training equations
%For an I-H-O node topology, the number of unknown weights is
Nw = (I+1)*H+(H+1)*O
% Therefore, Ntrneq > Nw <==> H <= Hub where
Hub = -1+ceil((Ntrneq-O)/(I+O+1)) % 9
Try to minimize H while achieving an adjusted R-squared >= 0.99. I have posted many examples. Search on
greg patternnet Ntrials R2a
You may also wish to use 10-fold crossvalidation to obtain more precise estimates of error rates.
Hope this helps.
Thank you for formally accepting my answer
Greg
2 件のコメント
Greg Heath
2014 年 6 月 18 日
The variety of results that you experienced result from the default randomness of trn/val/tst data division and random initial weights. Initializing the RNG to a specified initial state will yield repeatable results.
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