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

• 作者: Weizi Li Yang Yu Yueshan Xiong Zeyu Xiong
• 来源期刊: 计算机、材料和连续体(英文)
• 学科: 工学
• 关键词: LOGISTIC regression MULTI-CLASSIFICATION KERNEL function DENSITY estimation NON-EQUILIBRIUM
• 年，卷（期）: 2019,（7）
• 页码范围: 103-117
• 页数: 15页
• 分类号: TP3

摘要

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.