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Issues concerning intelligent data analysis occurring in machine learning areinvestigated. A scheme for synthesizing correct supervised classificationprocedures is proposed. These procedures are focused on specifying partialorder relations on sets of feature values; they are based on a generalizationof the classical concepts of logical classification. It is shown that learningthe correct logical classifier requires an intractable discrete problem to besolved. This is the dualization problem over products of partially orderedsets. The matrix formulation of this problem is given. The effectiveness of theproposed approach to the supervised classification problem is illustrated onmodel and real-life data.

Logical Classification of Partially Ordered Data