An application of the rule induction algorithm MODLEM
to construct multiple classifiers is studied. Two different such classifiers
are considered: the bagging approach, where classifiers are generated
from different samples of the learning set, and the n
2-classifier, which
is specialized for solving multiple class learning problems. This paper
reports results of an experimental comparison of these multiple classifiers
and the single, MODLEM based, classifier performed on several data sets.