Entropy-Based Greedy Algorithm for Decision Trees Using Hypotheses

Mohammad Azad; Igor Chikalov; Shahid Hussain; Mikhail Moshkov

doi:10.3390/e23070808

Entropy-Based Greedy Algorithm for Decision Trees Using Hypotheses

Mohammad Azad, Igor Chikalov, Shahid Hussain, Mikhail Moshkov

Research output: Contribution to journal › Article › peer-review

14 Scopus citations

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.

Original language	English (US)
Pages (from-to)	808
Journal	Entropy
Volume	23
Issue number	7
DOIs	https://doi.org/10.3390/e23070808
State	Published - Jun 25 2021

ASJC Scopus subject areas

Physics and Astronomy (miscellaneous)
Statistical and Nonlinear Physics

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10.3390/e23070808

https://repository.kaust.edu.sa/bitstream/10754/669787/1/entropy-23-00808.pdf

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title = "Entropy-Based Greedy Algorithm for Decision Trees Using Hypotheses",

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.",

author = "Mohammad Azad and Igor Chikalov and Shahid Hussain and Mikhail Moshkov",

note = "KAUST Repository Item: Exported on 2021-06-28 Acknowledgements: Research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST), including the provision of computing resources. The authors are greatly indebted to the anonymous reviewers for useful comments and suggestions.",

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AU - Chikalov, Igor

AU - Hussain, Shahid

AU - Moshkov, Mikhail

N1 - KAUST Repository Item: Exported on 2021-06-28 Acknowledgements: Research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST), including the provision of computing resources. The authors are greatly indebted to the anonymous reviewers for useful comments and suggestions.

PY - 2021/6/25

Y1 - 2021/6/25

N2 - 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.

AB - 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.

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JO - Entropy

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Entropy-Based Greedy Algorithm for Decision Trees Using Hypotheses

Abstract

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