通过超参数优化改进的决策树模型,使用修改后的灰狼优化进行糖尿病分类
Muhammad Sam'an1,2, Farikhin3, Muhammad Munsarif1
1Department of Informatics, Universitas Muhammadiyah Semarang, Semarang, Indonesia.
Computer methods in biomechanics and biomedical engineering
|February 13, 2025
概括
一个新的修改后的灰狼优化 (MGWO) 提高了糖尿病预测的准确性. 这种增强的算法优化了决策树,优于标准的灰狼优化和遗传算法,以更好地检测疾病.
科学领域:
- 医疗信息学 医疗信息学
- 计算智能是一种计算智能.
- 机器学习 机器学习
背景情况:
- 糖尿病是一种全球性健康问题,患病率越来越高.
- 早期检测和准确的预测对于预防严重并发症至关重要.
- 传统的决策树模型在处理大数据集时遇到困难.
研究的目的:
- 为了提高糖尿病预测的决策树 (DT) 性能.
- 解决灰狼优化 (GWO) 在搜索空间探索中的局限性.
- 引入一个修改的GWO (MGWO) 带有Levy分配,以提高优化.
主要方法:
- 使用元启发算法对决策树进行超参数优化.
- 实施灰狼优化 (GWO) 和一个修改版本 (MGWO) 结合了Levy飞行.
- 用基因算法 (GA) 进行性能评估的比较分析.
主要成果:
- 经过修改的GWO (MGWO) 实现了0.8498的健身值,超过了GWO (0.8373) 和GA (0.8492).
- 与标准GWO相比,MGWO和GA表现出相似且更高的分类准确度.
- 拟议的MGWO方法显示了比现有方法更好的性能.
结论:
- 修改后的灰狼优化 (MGWO) 有效地提高了糖尿病预测的决策树性能.
- 在MGWO中整合强制飞行扩大了搜索能力,减轻了过早的融合.
- 进一步的研究应该探索狼群大小对优化和准确性的影响.
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