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Abstract:
The committee approach has been proposed for reducing model
uncertainty and improving generalization performance. The advantage
of committees depends on (1) the performance of individual members
and (2) the correlational structure of errors between members. This
paper presents an input grouping technique for
designing
a
heterogeneous committee.
With this technique, all input variables are first grouped based
on their mutual information. Statistically similar variables are
assigned to the same group. Each member's input set is then formed
by input variables extracted from different groups. Our
t designed committees
have less correlation between its members, since each member
observes different input variable combinations. The individual
member's feature sets contain less redundant information, because
highly correlated variables will not be combined together. The
member feature sets contain almost complete information, since each
set contains a feature from each information group. An empirical
study for a noisy and nonstationary economic forecasting problem
shows that committees constructed by our proposed technique
outperform committees formed using several existing
techniques.
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