We describe an experimental study of Option Decision Trees with majority votes. Option Decision Trees generalize regular decision trees by allowing option nodes in addition to decision nodes; such nodes allow for several possible tests to be conducted instead of the commonly used single test. Our goal was to explore when option nodes are most useful and to control the growth of the trees so tha...

Decision tree induction is certainly among the most applicable learning techniques due to its power and simplicity. Howevel; learning decision trees from large datasets, particularly in data mining, is quite different from learning from small or moderately sized datasets. When learning from large datasets, decision tree induction programs often produce very large trees. How to visualize efficie...

The main goal of this paper is to describe a new pruning method for solving decision trees and game trees. The pruning method for decision trees suggests a slight variant of decision trees that we call scenario trees. In scenario trees, we do not need a conditional probability for each edge emanating from a chance node. Instead, we require a joint probability for each path from the root node to...

In this paper, we study arbitrary infinite binary information systems each of which consists an set elements and two-valued non-constant functions (attributes) defined on the elements. We consider notion a problem over system, is described by finite number attributes: for given element, should determine values these attributes. As algorithms solving, decision trees that use attributes from cons...