  We investigate at decision trees that incorporate both traditional queries
based on one attribute and queries based on hypotheses about the values of all
attributes. Such decision trees are similar to ones studied in exact learning,
where membership and equivalence queries are allowed. We present greedy
algorithms based on diverse uncertainty measures for construction of above
decision trees and discuss results of computer experiments on various data sets
from the UCI ML Repository and randomly generated Boolean functions. We also
study the length and coverage of decision rules derived from the decisiontrees
constructed by greedy algorithms.
