  Issues concerning intelligent data analysis occurring in machine learning are
investigated. A scheme for synthesizing correct supervised classification
procedures is proposed. These procedures are focused on specifying partial
order relations on sets of feature values; they are based on a generalization
of the classical concepts of logical classification. It is shown that learning
the correct logical classifier requires an intractable discrete problem to be
solved. This is the dualization problem over products of partially ordered
sets. The matrix formulation of this problem is given. The effectiveness of the
proposed approach to the supervised classification problem is illustrated on
model and real-life data.
