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- Title
Improved Logistic Regression Algorithm Based on Kernel Density Estimation for Multi-Classification with Non-Equilibrium Samples.
- Authors
Yang Yu; Zeyu Xiong; Yueshan Xiong; Weizi Li
- Abstract
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.
- Subjects
LOGISTIC regression analysis; PROBABILITY theory; SUPPORT vector machines; SPEECH perception; LEAST squares; HANDWRITING recognition (Computer science); KERNEL functions; ALGORITHMS
- Publication
Computers, Materials & Continua, 2019, Vol 61, Issue 1, p103
- ISSN
1546-2218
- Publication type
Article
- DOI
10.32604/cmc.2019.05154