Entropy-Based Greedy Algorithm for Decision Trees Using Hypotheses

Thumbnail
Name:
entropy-23-00808.pdf
Size:
250.4Kb
Format:
PDF
Description:
Publisher's version
Download
Type
Article
Authors
Azad, Mohammad cc
Chikalov, Igor cc
Hussain, Shahid cc
Moshkov, Mikhail cc
KAUST Department
Applied Mathematics and Computational Science Program
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Extensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
Date
2021-06-25
Submitted Date
2021-05-30
Permanent link to this record
http://hdl.handle.net/10754/669787

Metadata
Show full item record
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.
Citation
Azad, M., Chikalov, I., Hussain, S., & Moshkov, M. (2021). Entropy-Based Greedy Algorithm for Decision Trees Using Hypotheses. Entropy, 23(7), 808. doi:10.3390/e23070808
Sponsors
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.
Publisher
MDPI AG
Journal
Entropy
DOI
10.3390/e23070808
Additional Links
https://www.mdpi.com/1099-4300/23/7/808
Collections
Articles; Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Except where otherwise noted, this item's license is described as This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.