Neural network with limited datasets

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rIznaldi 2014-6-18

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评论： rIznaldi 2014-7-11

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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Answer 1

Greg Heath 2014-6-18

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编辑：Greg Heath 2014-6-18

在 MATLAB Online 中打开

 [ 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

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Greg Heath 2014-6-18

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.

rIznaldi 2014-7-11