MATLAB: How to remove outlier from training data before training artificial neural networks

Deep Learning Toolboxoutlier

If there is any method to do this in Matlab kindly share ?

Best Answer

Use zscore or mapstd to transform to zero-mean/unit-variance.

Plot

Decide how many standard deviations from the mean to set the outlier threshold.

Hope this helps.

Thank you for formally accepting my answer

Greg

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MATLAB: How to fix minmum mse requirement for neural network training mannually rather using default requirement

 [ I N ] = size(x)
 [ O N ] = size(t)

% Reference MSEs (From Naïve Constant Output Model y00 = mean(ttrn,2))

 Ntrn      = N-2*(0.15*N)       % Default no. of training examples
 MSEtrn00  = mean(var(ttrn',1)) % Biased training target variance (divide by Ntrn)
 MSEtrn00a = mean(var(ttrn',0)  % Degree of freedom "A"djusted (divide by Ntrn-1)

% Number of estimation degrees of freedom (See Wikipedia)

 Ntrneq    = Ntrn*O             % prod(size(ttrn)) is number of training equations
 Nw        = (I+1)*H+(H+1)*O    % Number of unknown weights for an I-H-O node topology
 Ndof      = Ntrneq-Nw          % Not useful when Nw >= Ntrneq
 etrn    = ttrn-ytrn;           % Training error
 MSEtrn  = sse(etrn)/Ntrneq     % Biased MSE
         = mse(etrn)
 MSEtrna = sse(etrn)/Ndof       % DOFA  MSE
         = Ntrneq*MSEtrn/Ndof

% Normalized MSEs

 NMSEtrn  = MSEtrn/MSEtrn00
 NMSEtrna = MSEtrna/MSEtrn00a

% Practical training goal

 NMSEtrna <= 0.01  % Independent of target scaling
 net.trainParam.goal = 0.01*Ndof*MSEtrn00a/Ntrneq

Hope this helps.

Thank you for formally accepting my answer

Greg

MATLAB: Optimum MSE for neural networks

Impossible to tell without knowing or being able to calculate the normalized degree-of-freedom-adjusted (DOFA) training subset MSE (NMSEtrna) or the corresponding Rsquare or coefficient of determination (Rsqtrna = 1-NMSEtrna).

Search Rsquare and "coefficient of determination" on Google and Wikipedia.

Calculations can be made as follows

 [ I N ]    = size(x)
 [ O N ]    = size(t) = size(y)
 Ntrn      = No. of training examples ( Ntrn ~ 0.7*N is default )
 Ntrneq    = Ntrn*O  % No. of training equations
 MSEtrn00  = mean(var(ttrn',1))% avg training target variance
 SSEtrn    = sse(ttrn-ytrn)
 MSEtrn    = SSEtrn/Ntrneq
 NMSEtrn   = MSEtrn/MSEtrn00
 Rsqtrn    = 1 - NMSEtrn

Adjustments ("a") for degrees of freedom lost when evaluating the net with the same data that was used to estimate the weights:

 H         = number of hidden nodes
 Nw       = (I+1)*H+(H+1)*O  %  No. of unknown weights
 Ndof     =  Ntrneq - Nw     % No. of DOF
 MSEtrn00a = mean(var(ttrn',0))

NOTE: If there are more unknown weights than equations, Ndof < 0 and special methods like trainbr and/or regularization are required. Otherwise,

For DOFA with Ndof > 0:

 MSEtrna   = SSEtrn/Ndof
 NMSEtrna  = MSEtrna/MSEtrn00a
 Rsqtrna   = 1 - NMSEtrna

For many problems an appropriate training goal is

 R2sqtrna >= 0.99

 MSEtrn <= MSEtrngoal = 0.01*max(Ndof,0)*MSEtrn00a/Ntrneq

Hope this helps.

Thank you for formally accepting my answer

Greg

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MATLAB: How to fix minmum mse requirement for neural network training mannually rather using default requirement

MATLAB: Optimum MSE for neural networks

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