K′ times k-means logistic regression algorithm for imbalanced classification

Abstract

Because of its simplicity and high efficiency, logistic regression algorithm has been widely applied in the field of data mining. However, logistic regression classifiers always show poor performance when the categories are imbalanced. In this paper, a new algorithm named $\mathrm{K}\prime$KLR ($k\prime$ times k-means logistic regression) is proposed to deal with the imbalanced classification problem. Compared to other traditional methods, the effectiveness of the proposed algorithm is verified through experiments based on simulated data and real data.

Keywords:

• Imbalanced classifiers
• Imbalanced data
• K-means
• Logistic regression

Notes

1 https://www.kaggle.com/janiobachmann/credit-fraud-dealing-with-imbalanced-datasets/data?select=creditcard.csv

Additional information

Funding

This work was supported by National Natural Science Foundation of China (NNSF) of China (11371051).