Entropy-Based Greedy Algorithm for Decision Trees Using Hypotheses
Mohammad Azad,
Igor Chikalov,
Shahid Hussain,
Mikhail Moshkov
Affiliations
Mohammad Azad
Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72441, Saudi Arabia
Igor Chikalov
Intel Corporation, 5000 W Chandler Blvd, Chandler, AZ 85226, USA
Shahid Hussain
Computer Science Program, Dhanani School of Science and Engineering, Habib University, Karachi 75290, Pakistan
Mikhail Moshkov
Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
In this paper, we consider decision trees that use both conventional queries based on one attribute each and queries based on hypotheses of values of all attributes. Such decision trees are similar to those studied in exact learning, where membership and equivalence queries are allowed. We present greedy algorithm based on entropy for the construction of the above decision trees and discuss the results of computer experiments on various data sets and randomly generated Boolean functions.
