Ensembles of decision rules for solving binary classification problems in the presence of missing values
[ 1 ] Instytut Informatyki (II), Wydział Informatyki i Zarządzania, Politechnika Poznańska | [ P ] employee
2006
paper
english
EN In this paper, we consider an algorithm that generates an ensemble of decision rules. A single rule is treated as a specific subsidiary, base classifier in the ensemble that indicates only one of the decision classes. Experimental results have shown that the ensemble of decision rules is as efficient as other machine learning methods. In this paper we concentrate on a common problem appearing in real-life data that is a presence of missing attributes values. To deal with this problem, we experimented with different approaches inspired by rough set approach to knowledge discovery. Results of those experiments are presented and discussed in the paper.
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