Entropy-Based Greedy Algorithm for Decision Trees Using Hypotheses
Mohammad Azad, Igor Chikalov, Shahid Hussain, Mikhail Moshkov
Abstract
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.
Topics & Concepts
Greedy algorithmDecision treeEntropy (arrow of time)Computer scienceDecision tree learningEquivalence (formal languages)MathematicsID3 algorithmTheoretical computer scienceAlgorithmMachine learningDiscrete mathematicsIncremental decision treePhysicsQuantum mechanicsRough Sets and Fuzzy LogicMachine Learning and AlgorithmsMachine Learning and Data Classification