Encoding missing values

[Thierry.Denoeux@hds.univ-compiegne.fr]

Dear Lutz, dear connectionists,

In a recent mailing, Lutz Prechelt mentioned the interesting problem of how 
to encode attributes with missing values as inputs to a neural network.
I have recently been faced to that problem while applying neural nets to
rainfall prediction using weather radar images. The problem was to classify
pairs of "echoes" -- defined as groups of connected pixels with reflectivity
above some threshold -- taken from successive images as corresponding to
the same rain cell or not. Each pair of echoes was discribed by a list of
attributes. Some of these attributes, refering to the past of a sequence, were
not defined for some instances. To encode these attributes with potentially
missing values, we applied two different methods actually suggested by Lutz:

- the replacement of the missing value by a "best-guess" value 
- the addition of a binary input indicating whether the corresponding attribute
  was present or absent.

Significantly better results were obtained by the second method.

This work was presented at ICANN'93 last september:

X. Ding, T. Denoeux & F. Helloco (1993). Tracking rain cells in radar images
using multilayer neural networks. In Proc. of ICANN'93, Springer-Verlag, 
p. 962-967.



Thierry Denoeux

 
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| tdenoeux at hds.univ-compiegne.fr  Thierry DENOEUX                        |
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