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Mefodovskiy asked . 2021-07-23

Feature selection using neural network

Hello. I need to select the most informative input parameters using a neural network. I tried using autoencoder, but I did not succeed. Can you advise me(or give ready code if it is exist) how I can determine the most important parameters in the input sample?
INPUT matrix: 114(features)x426(samples), outputs: 62x426.
Data look so: inputs (1; 85,4794464111327; 56,4613265991210; 0; 0; 0; 0; 29,0181198120117; 0; 51,6929779052734; 87; 50; 32; 18; 137,172424316406 ...),
outputs (0,731800019741058; 0,555100023746491; 0,214200004935265; 0,00219999998807907; 0,0132999997586012; 0,00289999996311963; 0,00689999992027879; 0,00130000000353903; 0,00150000001303852; 0,0496999993920326; 0,0828000009059906; 0,142299994826317...). It is one sample of 426.

matlab , neural network , neural networks , feature selection

Expert Answer

John Williams answered . 2024-04-27 02:00:45

The most important of I inputs in "I"nput matrices with I rows and N columns depends on the "O"utput target matrix with O rows and N columns.
 
If input and output variables are separately standardized to zero-mean/unit-variance variables, inputs can be ranked more easily via stepwise selection algorithms. The most common of these are forward selection and backward selection. However, there are also combined forward/backward and backward/forward algorithms.
 
I tend to rely on the stepwise selection algorithms for linear transformations. These are available from the MATLAB function STEPWISEFIT.
 
A command line search yields
 
>> lookfor stepwise
 
 addedvarplot    - Create added-variable plot for stepwise regression
stepwise         - Interactive tool for stepwise regression.
stepwisefit      - Fit regression model using stepwise regression
stepwiseglm      - Create generalized linear regression model by stepwise regression.
stepwiselm       - Create 1st order linear regression model by stepwise regression.
The help and doc commands yield explanations and examples.
 
Another possibility is to use a 2nd order linear regression model by adding crossproducts and squares.
 
Most likely there are more sophisticated algorithms available. However, I have never had the need.


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