用卷积神经网络将表格数据转换成图像的转换器进行比较分析,用于使用卷积神经网络对阿尔博病毒进行分类
Leonides Medeiros Neto1, Sebastião Rogerio da Silva Neto1, Patricia Takako Endo1
1Universidade de Pernambuco, Recife, Pernambuco, Brazil.
PloS one
|December 8, 2023
概括
深度学习模型,特别是卷积神经网络 (CNN),在分析转换为图像的表格数据时,可以实现与传统机器学习 (ML) 算法 (如XGBoost) 相比的性能.
科学领域:
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
- 数据分析 数据分析
背景情况:
- 表格式数据被广泛使用,但通常使用基于树的机器学习 (ML) 算法进行分析.
- 深度学习 (DL) 模型在非结构化数据 (图像,文本) 中表现出色,但在表式数据集中不太常见.
- 将表格数据转换为图像可以使用卷积神经网络 (CNN) 进行分析.
研究的目的:
- 将表格数据转换为图像的不同方法进行比较.
- 为了评估CNN在图像转换表格数据上的性能,与优化的ML模型 (XGBoost) 相比.
- 使用随机搜索优化CNN性能,并将其与ML优化技术进行比较.
主要方法:
- 用各种工具将表格式数据集转换为图像表示.
- 一个卷积神经网络 (CNN) 在这些图像表示上受过训练.
- 对CNN的表现进行了比较,对XGBoost模型进行了训练,该模型使用原始表格数据进行训练,两种模型都进行了优化.
主要成果:
- 一个单个卷积层的基本CNN实现了与优化XGBoost模型可比的性能指标.
- 使用网格搜索和功能选择优化了XGBoost模型.
- 使用随机搜索对CNN的进一步优化并没有产生显著的性能改善.
结论:
- 卷积神经网络 (CNN) 在将数据转换为图像格式时显示出分析表格数据的潜力.
- 即使是简单的CNN架构也可以与已建立的ML算法竞争,例如XGBoost用于表式数据任务.
- 在图像转换的表格数据上,CNN的有效性需要进一步研究优化策略.
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