To combat multi-class imbalanced problems by means of over-sampling and boosting techniques-Reference-Cited by-同舟云学术

To combat multi-class imbalanced problems by means of over-sampling and boosting techniques

Published:2014-04-30 Issue:12 Volume:19 Page:3369-3385
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Abdi Lida,Hashemi Sattar

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

http://link.springer.com/content/pdf/10.1007/s00500-014-1291-z.pdf

Reference65 articles.

1. Alcal J, Fernndez A, Luengo J, Derrac J, Garca S, Snchez L, Herrera F (2010) KEEL data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J Mult Valued Log Soft Comput

2. Alibeigi M, Hashemi S, Hamzeh A (2012) DBFS: an effective density based feature selection scheme for small sample size and high dimensional imbalanced data sets. Data Knowl Eng

3. Aczl J, Darczy Z (1975) On measures of information and their characterizations. New York

4. Bishop CM (2007) Pattern recognition and machine learning (information science and statistics)

5. Bradley AP (1997) The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognit 30(7):1145–1159

Cited by 27 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Clustering-Based Oversampling Algorithm for Multi-class Imbalance Learning;Journal of Classification;2024-08-22

2. Strategy of oversampling geotechnical parameters through geostatistical, SMOTE, and CTGAN methods for assessing susceptibility of landslide;Landslides;2023-10-19

3. Mining Multiple Class Imbalanced Datasets Using a Specialized Balancing Algorithm and the Adaboost Technique;Computational Collective Intelligence;2023

4. Balancing Agents for Mining Imbalanced Multiclass Datasets – Performance Evaluation;Computational Science – ICCS 2023;2023

5. An oversampling method for imbalanced data based on spatial distribution of minority samples SD-KMSMOTE;Scientific Reports;2022-10-07