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通过增强超参数调来结合基因和基于CNN模型的图像分类.

Wajahat Hussain1, Muhammad Faheem Mushtaq2, Mobeen Shahroz2

  • 1Department of Computer Science, The Islamia University of Bahawalpur, Bahawalpur, Punjab, Pakistan.

Scientific reports
|January 6, 2025
PubMed
概括
此摘要是机器生成的。

整体遗传算法和卷积神经网络 (EGACNN) 通过优化超参数显著提高了图像分类的准确性. 这种方法达到99.91%的准确性,优于其他深度学习模型.

关键词:
深度学习是一种深度学习.遗传算法 遗传算法 遗传算法图像处理 图像处理模型优化模型优化这是光学字符识别系统.

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 超参数优化对于提高图像分类模型性能至关重要.
  • 过度配置是深度学习中常见的挑战,需要平衡的模型复杂性和概括性.
  • 像CNN,RNN,AlexNet,ResNet和VGG这样的现有模型在实现最佳性能方面存在限制.

研究的目的:

  • 通过使用集体学习和遗传算法,提出一个增强的图像分类模型.
  • 微调卷积神经网络 (CNN) 的超参数,以提高精度和效率.
  • 为了利用整体方法的优势,获得优异的图像分类结果.

主要方法:

  • 开发了一个集体遗传算法和卷积神经网络 (EGACNN) 模型.
  • 整合了基因算法 (GA) 与CNN,使用堆叠来进行超参数优化.
  • 调整了CNN的超参数,包括层数,内核大小,学习率,中断率和批量大小.
  • 利用修改的国家标准与技术研究所 (MNIST) 数据集进行培训和评估.

主要成果:

  • 拟议的EGACNN模型实现了最高准确率99.91%.
  • 综合CNN和尖端神经网络 (CSNN) 模型的准确率达到了99.68%.
  • 与独立的CNN,RNN,AlexNet,ResNet和VGG模型相比,EGACNN和CSNN模型显示出更高的性能.

结论:

  • 组合方法,特别是EGACNN,在图像分类准确性方面提供了显著的改进.
  • 使用GA的超参数优化有效地提高了深度学习模型的性能,并减少了手工工作.
  • 拟议的EGACNN模型代表了图像分类任务的优越替代方案.