This algorithm is applicable to decision tables with many-valued decisions where each row
is labeled with a set of decisions. For a given row, we should find a decision from the set
attached to this row. We use an uncertainty measure which is the number of boundary
subtables. We present also experimental results for data sets from UCI Machine Learning
Repository for proposed approach and approach based on generalized decision.