Minimizing Depth of Decision Trees with Hypotheses

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Type
Conference Paper
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
2021-09-16
Online Publication Date
2021-09-16
Print Publication Date
2021
Permanent link to this record
http://hdl.handle.net/10754/671321

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Abstract
In this paper, we consider decision trees that use both conventional queries based on one attribute each and queries based on hypotheses about values of all attributes. Such decision trees are similar to ones studied in exact learning, where membership and equivalence queries are allowed. We present dynamic programming algorithms for minimization of the depth of above decision trees and discuss results of computer experiments on various data sets and randomly generated Boolean functions.
Citation
Azad, M., Chikalov, I., Hussain, S., & Moshkov, M. (2021). Minimizing Depth of Decision Trees with Hypotheses. Lecture Notes in Computer Science, 123–133. doi:10.1007/978-3-030-87334-9_11
Sponsors
Research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST). The authors are greatly indebted to anonymous reviewers for useful comments and suggestions.
Publisher
Springer International Publishing
Conference/Event name
International Joint Conference on Rough Sets 2021
DOI
10.1007/978-3-030-87334-9_11
Additional Links
https://link.springer.com/10.1007/978-3-030-87334-9_11
Collections
Conference Papers; Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division