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相关实验视频

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通过Adam和RanAdam的超参数调来提高分类器性能,以从微阵列数据中检测肺癌,以追求精确度.

Karthika M S1, Harikumar Rajaguru2, Ajin R Nair2

  • 1Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam 638401, India.

Bioengineering (Basel, Switzerland)
|April 27, 2024
PubMed
概括

这项研究通过应用快速里埃转换 (FFT) 和龙优化来增强使用基因表达数据的癌症分类. 支持矢量机 (SVM) 分类器实现了98.343%的准确性,改善了癌症亚型的识别.

关键词:
亚当和RanAdam在调音中进行调音.在 DimReRe 中使用.金融金融公司 (FFT)在MAGE数据中,MAGE数据癌症分类 癌症分类 癌症分类肺癌检测 肺癌检测 肺癌检测混合物模型模型的混合物模型

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科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 基因组学就是基因组学.

背景情况:

  • 微阵列基因表达分析对于癌症分类至关重要,但数据库复杂,杂和非线性.
  • 从大型,冗余的微阵列数据集中提取有意义的见解,在癌症研究中提出了重大挑战.

研究的目的:

  • 开发用于癌症分类的微阵列数据中减小维度和特征选择的有效方法.
  • 通过使用先进技术优化机器学习分类器来提高癌症分类的准确性.

主要方法:

  • 采用快速里埃转换 (FFT) 和混合模型 (MM) 来减少维度.
  • 使用龙优化算法进行特征选择.
  • 评估非线性回归,天真贝叶斯,决策树,随机森林和支持向量机 (SVM与RBF内核) 分类器,具有和没有特征选择.

主要成果:

  • SVM (RBF) 分类器,结合FFT尺寸缩小和Dragonfly特征选择,显示出卓越的性能.
  • 使用随机自适应时刻估计 (RanAdam) 的超参数调整进一步提高了分类器的准确性.
  • 优化的SVM (RBF) 模型在癌症分类中实现了最高准确率98.343%.

结论:

  • 整合FFT,Dragonfly优化和RanAdam调整的SVM (RBF) 提供了一个强大的方法,可以从微阵列数据中准确地进行癌症分类.
  • 这种方法有效地解决了基因表达数据集中的噪声和非线性挑战,增强了诊断潜力.