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Input Variable Selection
The problem of input variable selection is well known in the task of modeling real world
data. In many real world modeling problems, for example in the context of
biomedical, industrial, or environmental systems, a problem can occur when
developing multivariate models and the best set of inputs to use are not
known.
This is particularly true when using neural networks. In
this case, unrequired inputs can significantly increase learning complexity.
Input variable selection (IVS) is aimed at determining which input variables are
required for a model. The task is to determine a set of inputs which will lead
to an optimal model in some sense. Problems which can occur due to poor
selection of inputs include the following:
 As the input dimensionality increases, the
computational complexity and memory requirements of the model increase.
 Learning is more difficult with unrequired inputs.
 Misconvergence and poor model accuracy may result
from additional unrequired inputs.
 Understanding complex models is more difficult than
simple models which give comparable results.
The input variable selection method we have developed is based on performing a
statistical test between each of the input variable(s)
and the desired output from the model. In some situations there may be
dependence between input variables which leads to an overestimation of the
number of inputs required. One method to overcome this is to use independent
component analysis (ICA) as a preprocessing method.
In order to assess the dependence between inputs and the desired system
output, we use a method based on higher order cross moments, up to a
specified order among the individual terms, and normalized in such a manner as
to allow their direct comparison. This statistical measure can be used to
establish the independence or otherwise of nonGaussian signals. These cross
moments are defined between the inputs x_{1},x_{2},...,x_{n,}
individually at time t, and the target output y, with powers up to p=3. Not all cross terms are
used, but a selection. The model implements only instantaneous moments, without employing time delays,
however it is possible to use lagged regression vectors as inputs to achieve
this result. The resulting output is a score vector indicating the
dependence of each input on the output. This vector is then classified into to
classes using for example, the kmeans algorithm to give a binary classification vector.
Because the algorithm uses higher order statistics, it
is capable of finding inputs in nongaussian and nonlinear processes.
References

A.D.
Back and T.P. Trappenberg, "Selecting inputs for modelling using normalized
higher order statistics and independent component analysis", IEEE Trans. on
Neural Networks, Vol. 12, No. 3, pp. 612617, May, 2001. Click here to download the paper.


