Zusammenfassung
The expressive power, powerful search capability, and
the explicit nature of the resulting models make
evolutionary methods very attractive for supervised
learning applications in bioinformatics. However, their
characteristics also make them highly susceptible to
overtraining or to discovering chance relationships in
the data. Identification of appropriate criteria for
terminating evolution and for selecting an
appropriately validated model is vital. Some approaches
that are commonly applied to other modeling methods are
not necessarily applicable in a straightforward manner
to evolutionary methods. An approach to model selection
is presented that is not unduly computationally
intensive. To illustrate the issues and the technique
two bioinformatic datasets are used, one relating to
metabolite determination and the other to disease
prediction from gene expression data.
Nutzer