改进的随机森林分类模型与C5.0算法相结合,用于在非农业环境中分析植被特征
1College of Architecture, Nanjing Tech University, Nanjing City, 211800, China. 15195809009@163.com.
Scientific reports
|May 6, 2024
概括
这项研究引入了一种增强的随机森林算法,用于使用卫星数据对非农业植被进行分类,达到90.20%的准确性. 改进的模型有效处理复杂的数据,有助于保护农业生态系统和恢复生物多样性.
科学领域:
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
- 生态生态学 生态生态学
背景情况:
- 传统的随机森林算法与高维,杂的卫星数据作斗争,用于非农业植被的分类.
- 挑战包括高计算复杂性和低于最佳的分类性能.
研究的目的:
- 通过卫星数据提出一个增强的随机森林算法,以更好地使用卫星数据对非农业植被进行分类.
- 解决传统方法在高维和杂数据集中的局限性.
主要方法:
- 开发了一个增强的随机森林算法,集成了C5.0算法来进行特征选择.
- 采用基于包装概念的整体特征方法,以改善特征选择和模型多样性.
- 使用增强的植被指数 (EVI) 来估计植被覆盖面.
主要成果:
- 面向对象的随机森林模型在空中图像数据集上实现了94.02%的准确性.
- 拟议的算法在识别非农业植被特征方面达到90.20%的平均准确率.
- 它的表现优于BERT,FastText和CNN等其他模型,准确度从84.41%到88.33%不等.
结论:
- 增强的随机森林算法有效地分类非农业植被,提供更高的准确性和效率.
- 这些发现提供了支持农业生态系统保护和生物多样性恢复工作的科学证据.
- C5.0算法和EVI有助于进行可靠的特征选择和植被覆盖率估计.
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