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Improved Logistic Regression Algorithm Based on Kernel Density Estimation for Multi-Classification with Non-Equilibrium Samples

Yang Yu1, Zeyu Xiong1,*, Yueshan Xiong1, Weizi Li2

HPCL, School of Computer Science, National University of Defense Technology, Changsha,410073, China.
Department of Computer Science, University of North Carolina at Chapel Hill, North Carolina27599, USA.

* Corresponding Author: Zeyu Xiong. Email: email.

Computers, Materials & Continua 2019, 61(1), 103-118.


Logistic regression is often used to solve linear binary classification problems such as machine vision, speech recognition, and handwriting recognition. However, it usually fails to solve certain nonlinear multi-classification problem, such as problem with non-equilibrium samples. Many scholars have proposed some methods, such as neural network, least square support vector machine, AdaBoost meta-algorithm, etc. These methods essentially belong to machine learning categories. In this work, based on the probability theory and statistical principle, we propose an improved logistic regression algorithm based on kernel density estimation for solving nonlinear multi-classification. We have compared our approach with other methods using non-equilibrium samples, the results show that our approach guarantees sample integrity and achieves superior classification.


Cite This Article

Y. Yu, Z. Xiong, Y. Xiong and W. Li, "Improved logistic regression algorithm based on kernel density estimation for multi-classification with non-equilibrium samples," Computers, Materials & Continua, vol. 61, no.1, pp. 103–118, 2019.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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