In the paper, we study a greedy algorithm for construction of approximate decision trees.
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.

In the paper, we study a greedy algorithm for construction of decision trees. 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.
Experimental results for data sets from UCI Machine Learning Repository and randomly
generated tables are presented. We make a comparative study of the depth and average
depth of the constructed decision trees for proposed approach and approach based on …