An Extended Idea about Decision Trees

Decision trees have been widely recognized as a data mining and machine learning methodology that receives a set of attribute values as the input and generates a Boolean decision as the output. In this paper, I tried two experiments to demonstrate that the fundamental theory of decision trees can be extended to go beyond Boolean decisions. In the first experiment, the decision tree algorithm is extended to be capable of making three-way decisions. Likewise, in the second experiment, the decision tree algorithm is extended to be capable of making four-way decisions. As a result, a conclusion is reached that this extended idea about decision trees can be generalized to make n-way decisions.