On the maximal size of tree in a random forest

被引：0
|
作者：
Pavlov, Yuriy L. [1 ]
机构：
[1] RAS, Inst Appl Math Res, Karelian Res Ctr, Petrozavodsk, Russia
来源：
DISCRETE MATHEMATICS AND APPLICATIONS | 2024年 / 34卷 / 04期
关键词：
Galton-Watson forest; tree size; vertex degree; limit theorems;
D O I：
10.1515/dma-2024-0019
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
Galton-Watson forests consisting of N rooted trees and n non-root vertices are considered. The distribution of the forest is determined by that of critical branching process with infinite variance and regularly varying tail of the progeny distribution. We prove limit theorem for the maximal size of a tree in a forest as N, n -> infinity in such a way that n/N -> infinity. Our conditions are significantly wider than was previously known.
引用
收藏
页码：221 / 232
页数：12
相关论文
共 50 条
  • [21] Spanning tree size in random binary search trees
    Panholzer, A
    Prodinger, H
    ANNALS OF APPLIED PROBABILITY, 2004, 14 (02): : 718 - 733
  • [22] A Rapidly-Exploring Random Tree Algorithm with Reduced Random Map Size
    Lonklang, Aphilak
    Botzheim, Janos
    2023 9TH INTERNATIONAL CONFERENCE ON AUTOMATION, ROBOTICS AND APPLICATIONS, ICARA, 2023, : 356 - 361
  • [23] A general model for fuzzy decision tree and fuzzy random forest
    Zheng, Hui
    He, Jing
    Zhang, Yanchun
    Huang, Guangyan
    Zhang, Zhenjiang
    Liu, Qing
    COMPUTATIONAL INTELLIGENCE, 2019, 35 (02) : 310 - 335
  • [24] Weighted Hybrid Decision Tree Model for Random Forest Classifier
    Kulkarni V.Y.
    Sinha P.K.
    Petare M.C.
    Journal of The Institution of Engineers (India): Series B, 2016, 97 (2) : 209 - 217
  • [25] Extracting Interpretable Decision Tree Ensemble from Random Forest
    Gulowaty, Bogdan
    Wozniak, Michal
    2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2021,
  • [26] Formation Resistivity Prediction Using Decision Tree and Random Forest
    Ibrahim, Ahmed Farid
    Abdelaal, Ahmed
    Elkatatny, Salaheldin
    ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2022, 47 (09) : 12183 - 12191
  • [27] From Random Forest to an interpretable decision tree - An evolutionary approach
    Jurczuk, Krzysztof
    Czajkowski, Marcin
    Kretowski, Marek
    PROCEEDINGS OF THE 2023 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2023 COMPANION, 2023, : 291 - 294
  • [28] Formation Resistivity Prediction Using Decision Tree and Random Forest
    Ahmed Farid Ibrahim
    Ahmed Abdelaal
    Salaheldin Elkatatny
    Arabian Journal for Science and Engineering, 2022, 47 : 12183 - 12191
  • [29] Optimal Feature Set Size in Random Forest Regression
    Han, Sunwoo
    Kim, Hyunjoong
    APPLIED SCIENCES-BASEL, 2021, 11 (08):
  • [30] On the Optimal Size of Candidate Feature Set in Random forest
    Han, Sunwoo
    Kim, Hyunjoong
    APPLIED SCIENCES-BASEL, 2019, 9 (05):
12345