The effect of imbalanced data sets on LDA: A theoretical and empirical analysis

Authors:
Jigang Xie;Zhengding Qiu
Affiliations:
Institute of Information Science, Beijing Jiaotong University, Beijing 100044, PR China;Institute of Information Science, Beijing Jiaotong University, Beijing 100044, PR China
Venue:
Pattern Recognition
Year:
2007

Citing 4
Cited 8

Statistical Pattern Recognition: A Review

IEEE Transactions on Pattern Analysis and Machine Intelligence
Editorial: special issue on learning from imbalanced data sets

ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
SMOTE: synthetic minority over-sampling technique

Journal of Artificial Intelligence Research
The use of the area under the ROC curve in the evaluation of machine learning algorithms

Pattern Recognition

Do unbalanced data have a negative effect on LDA?

Pattern Recognition
Do unbalanced data have a negative effect on LDA?

Pattern Recognition
Classification of weld flaws with imbalanced class data

Expert Systems with Applications: An International Journal
MDS: a novel method for class imbalance learning

Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
Toward breast cancer survivability prediction models through improving training space

Expert Systems with Applications: An International Journal
Evolutionary undersampling for classification with imbalanced datasets: Proposals and taxonomy

Evolutionary Computation
An asymmetric classifier based on partial least squares

Pattern Recognition
A combined approach to tackle imbalanced data sets

International Journal of Hybrid Intelligent Systems

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper demonstrates that the imbalanced data sets have a negative effect on the performance of LDA theoretically. This theoretical analysis is confirmed by the experimental results: using several sampling methods to rebalance the imbalanced data sets, it is found that the performances of LDA on balanced data sets are superior to those of LDA on imbalanced data sets.