Improve accuracy of small data set using Neural Network

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Lia
Lia 2016-10-6
评论： Greg Heath 2016-10-8
Hello ,i have an input matrix 4x24( 4features of 24 patients which 10 are positive and 14 negative)and target 2x24 in eye(2) form for a binary problem classification .I use patternnet and network node topology 4-3-2 with the transfer functions in default(tansig) .The total accuracy is 66% .If i dont use the validation set the accuracy goes up 90%.Is that correct or overfitting? Is it possible to have unbalanced data? How can i improve accuracy in this tiny data set?I use this code for k fold cross validation for 10 repetitions .

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Greg Heath
Greg Heath 2016-10-7
[I N ] = input; % [ 4 24 ]
[O N ] = target;% [ 1 24 ]
Ntrn = N -2*round(0.15N) % 16 default
Ntrneq = Ntrn*O % 16 No. of training equations
Nw = (I+1)*H+(H+1)O = (I+O+1)*H+O % No. of unknown weights
% NO OVERFITTING Ntrneq >= Nw <==> H <= (Ntrneq-O)/(I+O+1)= 2.5
==> H = 3 is a slight overfitting ==> Using 15% validation set is justified .
But have you tried 10 trials each of
H = 2
or
MSEREG
or
TRAINBR ?
or
10-FOLD X-VALIDATION?
Hope this helps.
Greg
  2 个评论
Lia
Lia 2016-10-7
Thank you for your answer ,i use your tutorial for 10 fold X Val for my classification problem using patternnet instead of fitnet with training function 'trainlm' and H=3 . For 10 repetitions i have a mean accuracy 66%. If i change H=2 mean accuracy falls and the same happens if i use trainbr and trainscg.The change mse to Msereg in matlabR2012b seems to be ignored .I have attached the relevant code in CV.mat .Does it look ok with differentions that i made?
Greg Heath
Greg Heath 2016-10-8
The point of using MSEREG and/or TRAINBR is that you can use H >> 2.
Hope this helps.
Greg

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