메뉴 건너뛰기
Library Notice
Institutional Access
If you certify, you can access the articles for free.
Check out your institutions.
ex)Hankuk University, Nuri Motors
Log in Register Help KOR
Subject
Humanities
General Humanities History Philosophy Religious Studies Linguistics and Literature Other Humanities
Social science
General Social Science Business Management Tourism Education Military Science Library and Information Science Law Sociology Journalism and Media Studies Psychological Science Political Science and Diplomacy Geography Public Administration Economics International Trade Anthropology Other Social Science
Natural science
General Natural Science Physics Biology Life Sciences Mathematics/Statistics Astronomy Earth Science Chemistry Other Natural Science
Engineering
General Engineering Architectural Engineering Mechanical Engineering Industrial Engineering Material/Energy Engineering Electrical and Electronic Engineering Marine and Ocean Engineering Chemical Engineering Computer Science Other Engineering
Medical science
General Medical Science Nursing Science Veterinary Pharmacy Traditional Korean Medicine Basic Medicine Clinical Medicine Other Medicine
Agriculture and Fishery
General Agriculture and Fishery Agriculture Fishery Science Food Science Forestry Animal Husbandry Marine Transportation Studies Other Agriculture and Fishery
Art and Kinesiology
General Arts Design Fine Arts Cosmetology Photography Music Fashion Kinesiology Performing Media Arts Other Arts and Kinesiology
Interdisciplinary Studies
Interdisciplinary Research Science and Technology Studies Brain and Cognitive Science Technology Policy Other Interdisciplinary Studies

KOR

Correlated variable importance for random forests
Recommendations
Search
Questions

랜덤포레스트를 위한 상관예측변수 중요도

논문 기본 정보

Type
Academic journal
Author
Seung Beom Shin (고려대학교) Hyung Jun Cho (고려대학교)
Journal
한국통계학회 응용통계연구 Vol.34 No.2 KCI Accredited Journals
Published
2021.4
Pages
177 - 190 (14page)

Usage

cover
📌
Topic
📖
Background
🔬
Method
🏆
Result
Correlated variable importance for random forests
Ask AI
Recommendations
Search
Questions

Research history (2)

2021

Correlated variable importance for random forests
Seung Beom Shin ,  Hyung Jun Cho 응용통계연구 2021.04 Academic journal
랜덤포레스트를 위한 상관예측변수 중요도
신승범 통계학과 2021.01 Thesis
  • Are you curious about the follow-up research of this article?
  • You can check more advanced research results through related academic papers or academic presentations.
  • Check the research history of this article

Abstract· Keywords

Report Errors
Random forests is a popular method that improves the instability and accuracy of decision trees by ensembles. In contrast to increasing the accuracy, the ease of interpretation is sacrificed; hence, to compensate for this, variable importance is provided. The variable importance indicates which variable plays a role more importantly in constructing the random forests. However, when a predictor is correlated with other predictors, the variable importance of the existing importance algorithm may be distorted. The downward bias of correlated predictors may reduce the importance of truly important predictors. We propose a new algorithm remedying the downward bias of correlated predictors. The performance of the proposed algorithm is demonstrated by the simulated data and illustrated by the real data.
#random forests #variable importance #correlation #랜덤포레스트 #변수중요도 #상관관계

Contents

Abstract
1. 서론
2. 분류 변수중요도
3. 모의실험
4. 사례분석
5. 결론
References
요약

References (0)

Add References
If the reference is being serviced by DBpia, please register through [Add Reference]

Recommendations

It is an article recommended by DBpia according to the article similarity. Check out the related articles!

Related Authors

Frequently Viewed Together

Random projection ensemble adaptive nearest neighbor classification
Jongkyeong Kang ,  Myoungshic Jhun 응용통계연구 2021 .06
A Comparison of Ensemble Methods Combining Resampling Techniques for Class Imbalanced Data
Hee Jae Lee ,  Sung Im Lee 응용통계연구 2014 .06

Comments(0)

0

Write first comments.