where k is fixed and y is a variable of particular interest. For example x may be customer information for a credit card customer, and y a measure of how credit worthy the customer is. Given a new instance of x we wish to be able to predict the corresponding y. This is essentially a regression problem, though is usually called classification when y is a discrete variable. We call x the independ...

Decision trees find use in a wide range of application domains. They are used in many different disciplines including diagnosis, cognitive science, artificial intelligence, game theory, engineering and data mining. Decision Trees model has two goals: producing an accurate classifier and understanding the predictive structure of the problem. The classification accuracy of decision trees has been...

The tree provides a framework for the calculation of the expected value of each available alternative. The alternative with the maximum expected value is the best choice path based on the information and mind-set of the decisionmakers at the time the decision is made. This best choice path indicates the best overall alternative, including the best subsidiary decisions at future decision steps, ...

In the presentation, we consider extensions of dynamic programming approach to the study of decision trees as algorithms for problem solving, as a way for knowledge extraction and representation, and as classifiers which, for a new object given by values of conditional attributes, define a value of the decision attribute. These extensions allow us (i) to describe the set of optimal decision tre...

If the company builds a big plant, it must live with it whatever the size of market demand. If it builds a small plant, management has the option of expanding the plant in two years in the event that demand is high during the introductory period; while in the event that demand is low during the introductory period, the company will maintain operations in the small plant and make a tidy profit o...

The logic-based decision trees and decision rules methodology performs well across a wide range of data mining problems. It also has the advantage that the results can be expressed in terms that the end-user can easily understand. Directory • Table of