Abstract
Learning with imprecise or missing data has been a
major challenge for machine learning. A new approach
using Strongly Typed Genetic Programming is proposed
here, which uses extra computations based on other
input data to approximate the missing values. It
eliminates the need for pre-processing and makes use of
correlations between the input data. The decision
process itself and the handling of unknown data can be
extracted from the resulting program for an analysis
afterwards. Comparing it to an alternative approach on
a simple example shows the usefulness of this
approach.
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