An experimental evaluation of improving rule based classifiers with two approaches that change representations of learning examples
Improving the classification performance of decision rules induced from examples is discussed in this paper. As standard rule based classifiers may not work well enough for some complex learning tasks, we consider an extension of the rule induction algorithm MODLEM with two approaches that change the original representation of the learning examples. The first approach selects the subset of the most relevant attributes with the wrapper model while the other modifies the presence of some learning examples in the data by the bagging technique. The aim of this study is to experimentally evaluate on several data sets how these two different approaches improve the classification performance of the rule sets induced by the MODLEM algorithm.
classification rule induction the modlem algorithm machine learning attribute selection multiple classifiers the bagging approach