在档案管理中,最小方位方法的识别效果
Caichang Ding1,2, Hui Liang3, Na Lin3
1School of Computer and Information Science, Hubei Engineering University, Xiaogan, 432000, China.
Heliyon
|October 9, 2023
概括
本研究介绍了用于有效档案管理的模糊最小平方支持向量机器 (FLS-SVM). FLS-SVM实现了高分类准确性,大大改善了档案识别和分类流程.
科学领域:
- 信息科学 信息科学 信息科学
- 计算机科学 计算机科学
- 机器学习 机器学习
背景情况:
- 在数字信息时代,有效的档案管理至关重要.
- 准确的档案识别和分类对于系统开发至关重要.
- 现有的方法在处理复杂的非线性数据模式时可能面临挑战.
研究的目的:
- 开发一个先进的分类器,以改善档案的识别和分类.
- 增强支持矢量机器 (SVM) 对于存档数据的功能.
- 引入一种结合模糊逻辑和LS-SVM的新方法.
主要方法:
- 该研究使用最小平方支向量机 (LS-SVM) 算法.
- 一个新的波形函数被纳入,以提高分类器的性能.
- 通过交叉验证技术优化内核参数.
- 模糊理论与LS-SVM集成,以创建模糊最小平方支持向量机 (FLS-SVM).
主要成果:
- 在档案数据集上,FLS-SVM分类器显示分类准确率为98.7%.
- 提出的方法实现了仅为0.26%的低损失率.
- 使用波形函数作为内核导致平均分类器准确率为98.38%.
结论:
- 在档案管理,识别和分类方面,FLS-SVM方法被证明是有效和可行的.
- 模糊理论和LS-SVM的整合增强了不可分割数据的分类.
- 这项研究验证了最小方程适配方法在档案数据处理中的实用性.
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