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[公式：见正文]-改进的非平行支持向量机。

[Formula: see text]-Improved nonparallel support vector machine.

作者信息

Sun Fengmin, Lian Shujun

机构信息

School of Management Science, Qufu Normal University, Rizhao, China.

出版信息

Sci Rep. 2022 Oct 25;12(1):17855. doi: 10.1038/s41598-022-22559-5.

DOI:10.1038/s41598-022-22559-5
PMID:36284146
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9596739/
Abstract

In this paper, a [Formula: see text]-improved nonparallel support vector machine ([Formula: see text]-IMNPSVM) is proposed to solve binary classification problems. In this model, we use related ideas of [Formula: see text]-support vector machine([Formula: see text]-SVM), the parameter [Formula: see text] is introduced to control the limits of the support vectors percentage. In the objective function, the parameter [Formula: see text] is increased to ensure that [Formula: see text]-band is kept as small as possible. It has played a great role in the classification of unbalanced data sets. On the basis of maximizing the interval between two classes, [Formula: see text]-IMNPSVM can fully fit the distribution of data points in the class by minimizing the [Formula: see text]-band, which enhances the generalization ability of the model. The results on the benchmark datasets testify that the proposed model has a good effect on the classification accuracy.

摘要

本文提出了一种[公式：见正文]改进的非平行支持向量机（[公式：见正文]-IMNPSVM）来解决二分类问题。在该模型中，我们采用了[公式：见正文]-支持向量机（[公式：见正文]-SVM）的相关思想，引入参数[公式：见正文]来控制支持向量百分比的界限。在目标函数中，增加参数[公式：见正文]以确保[公式：见正文]-带尽可能小。它在不平衡数据集的分类中发挥了很大作用。在最大化两类之间间隔的基础上，[公式：见正文]-IMNPSVM通过最小化[公式：见正文]-带能够充分拟合类中数据点的分布，从而增强了模型的泛化能力。基准数据集上的结果证明了所提出的模型在分类精度方面具有良好的效果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c962/9596739/0d03a78ff88c/41598_2022_22559_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c962/9596739/3afa317029cb/41598_2022_22559_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c962/9596739/d7f51bde3dd6/41598_2022_22559_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c962/9596739/f4a954e2b961/41598_2022_22559_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c962/9596739/9bc2eee9d23e/41598_2022_22559_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c962/9596739/4540beec69c8/41598_2022_22559_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c962/9596739/0d03a78ff88c/41598_2022_22559_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c962/9596739/3afa317029cb/41598_2022_22559_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c962/9596739/d7f51bde3dd6/41598_2022_22559_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c962/9596739/f4a954e2b961/41598_2022_22559_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c962/9596739/9bc2eee9d23e/41598_2022_22559_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c962/9596739/4540beec69c8/41598_2022_22559_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c962/9596739/0d03a78ff88c/41598_2022_22559_Fig6_HTML.jpg

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