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Motivation for this paper are classification problems in which data can not be clearly divided into positive and negative examples, especially data in which there is a monotone hierarchy (degree, preference) of more or less positive (negative) examples.  We use data expressing the impact of information systems on business competitiveness in a graded way. The research was conducted on a sample of more than 200 Slovak companies. Competitiveness is estimated by Porter’s model.    The induction is achieved via multiple use of two valued induction on alpha-cuts of graded examples with monotonicity axioms in background knowledge. We present results of ILP system ALEPH on above data interpreted as annotated rules. We comment on relations of our results to some statistical models.

Mining Rules from Monotone Classification Measuring Impact of Information Systems on Business Competitiveness