八个数据集的结构描述

数据集获取（Figshare）

• 源站标题：Structure description of the eight datasets.
• DOI：10.1371/journal.pone.0327748.t005
• 发布日期：2025-07-10
• 文件数：1 个
• 文件总体积：5.63 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Structure_description_of_the_eight_datasets_/29535008

命令行下载

curl -L -o "Table 5.xls" "https://ndownloader.figshare.com/files/56144498"

文件清单

• Table 5.xls（5.63 KB）

文件总体积 5.63 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

As the precise odor-sensing equipment, the electronic nose integrates multiple advanced and sensitive sensors that can identify wound infections non-invasively and rapidly by analyzing wound characteristic odor. To reduce the cost of sensors and improve or maintain e-nose’s performance, efficient optimization of sensor arrays is required. For this issue, we proposed a new sensor array optimization algorithm named Interfered Feature Elimination coupled with Feature Group Selection (IFE-FGS). In this method, the IFE algorithm first removed the bad sensor features; then the FGS algorithm determined the optimized sensor combination by gradually selecting the features in groups. The experimental results show the superiority of the IFE- FGS method on two bacteria datasets and six public gene expression profiling datasets. IFE- FGS achieves the classification accuracy of 93.95% and 94.94% in mean accuracy and max accuracy, respectively, on the bacteria dataset 2, which is significantly ahead of the comparison methods. Besides, our proposed method shows consistency and effectiveness. It achieves excellent performance, which takes two first-places, four second-places in mean accuracy, one first-place, and six second-places in max accuracy. Moreover, it also explores three novel and valuable discoveries for the electronic nose: 1) It can effectively identify biomarkers in the application. 2) It can effectively distinguish the degree of chemical components contributing to odors. 3) It can reveal the effective detection range of the targets.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0327748.t005（source: https://plos.figshare.com/articles/dataset/Structure_description_of_the_eight_datasets_/29535008）。