DATA SAMPLING METHODS FOR IMBALANCED CLASSIFICATION: A RANDOM FOREST STUDY FOR PREDICTING TREATMENT SWITCHING IN MULTIPLE SCLEROSIS

被引：0
作者：
Li, J. [1 ]
Huang, Y. [1 ]
Aparasu, R. R. [1 ]
机构：
[1] Univ Houston, Coll Pharm, Houston, TX 77030 USA
来源：
VALUE IN HEALTH | 2022年 / 25卷 / 07期
关键词：
D O I：
暂无
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
MSR32
引用
收藏
页码：S524 / S524
页数：1
相关论文
共 50 条
[31]   A priori synthetic over-sampling methods for increasing classification sensitivity in imbalanced data sets [J].
Rivera, William A. ;
Xanthopoulos, Petros .
EXPERT SYSTEMS WITH APPLICATIONS, 2016, 66 :124-135
[32]   Comparative Study Using Ensemble Methods and Sampling Techniques for Imbalanced Diabetes Data [J].
Khemisa, Hana ;
Dendani, Nadjette ;
Khedimi, Yasmine ;
Amara, Nour Djihane ;
Azizi, Nabiha .
INTELLIGENT SYSTEMS AND PATTERN RECOGNITION, ISPR 2024, PT II, 2025, 2304 :296-310
[33]   A Comprehensive Study on Ensemble-Based Imbalanced Data Classification Methods for Bankruptcy Data [J].
UlagaPriya, K. ;
Pushpa, S. .
PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON INVENTIVE COMPUTATION TECHNOLOGIES (ICICT 2021), 2021, :800-804
[34]   Two-Phase Stratified Random Forest for Paddy Growth Phase Classification: A Case of Imbalanced Data [J].
Suryono, Hady ;
Kuswanto, Heri ;
Iriawan, Nur .
SUSTAINABILITY, 2022, 14 (22)
[35]   Alterations to the Bootstrapping Process Within Random Forest: A Case Study on Imbalanced Bioinformatics Data [J].
Khoshgoftaar, Taghi M. ;
Fazelpour, Alireza ;
Dittman, David J. ;
Napolitano, Amri .
2015 IEEE 16TH INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION, 2015, :342-348
[36]   A hybrid sampling algorithm combining M-SMOTE and ENN based on Random forest for medical imbalanced data [J].
Xu, Zhaozhao ;
Shen, Derong ;
Nie, Tiezheng ;
Kou, Yue .
JOURNAL OF BIOMEDICAL INFORMATICS, 2020, 107
[37]   Classification of Real Imbalanced Cardiovascular Data Using Feature Selection and Sampling Methods: A Case Study with Neural Networks and Logistic Regression [J].
Bektas, Jale ;
Ibrikci, Turgay ;
Ozcan, Ismail Turkay .
INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS, 2017, 26 (06)
[38]   Comparison of IMF Selection Methods in Classification of Multiple Sclerosis EEG Data [J].
Kotan, Soner ;
Van Schependom, Jeroen ;
Nagels, Guy ;
Akan, Aydin .
2019 MEDICAL TECHNOLOGIES CONGRESS (TIPTEKNO), 2019, :228-231
[39]   Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment [J].
Wang, Yu .
POLITICAL ANALYSIS, 2019, 27 (01) :107-110
[40]   Predictors of treatment switching in the big multiple sclerosis data network - an update [J].
Spelman, T. ;
Magyari, M. ;
Sorensen, P. Soelberg ;
Butzkueven, H. ;
Van der Walt, A. ;
Vukusic, S. ;
Trojano, M. ;
Iaffaldano, P. ;
Pellegrini, F. ;
Hyde, R. ;
Stawiarz, L. ;
Manouchehrinia, A. ;
Hillert, J. .
MULTIPLE SCLEROSIS JOURNAL, 2022, 28 (3_SUPPL) :671-672
12345