糖尿病数据集比较结果
该数据集展示了OMWOA算法结合KELM在糖尿病诊断任务上的性能比较结果。
基本信息
资源简介
糖尿病数据集比较结果
【数据集背景】
本数据集源站标题为《Comparison results on the Diabetes dataset.》。源站首次发布：2025-06-03。
【数据内容】
源站原始描述：Swarm intelligence optimization algorithms represent a significant branch of nature-inspired computational methods, designed to solve complex optimization problems by simulating the collective behavior of biological systems. Whale optimization algorithm (WOA) is a newly developed meta-heuristic algorithm, which is mainly based on the predation behavior of humpback whales in the ocean. This study proposes an enhanced version of the WOA, named the Outpost-based Multi-population Whale Optimization…
【数据结构与技术规格】
文件数 1 个，合计 9.73 KB。Table 11.xls：application/vnd.ms-excel，9.73 KB。数据集 DOI：10.1371/journal.pone.0325272.t011。
【主题与分类】
源站学科分类：Space Science、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Mathematical Sciences not elsewhere classified。主题标签：newly developed meta、inspired computational methods、ieee cec 2017、experimental results demonstrate、art evolutionary algorithms、algorithm &# 8217、whale optimization algorithm、omwoa &# 8217、population enhanced mechanism、proposed algorithm。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/Comparison_results_on_the_Diabetes_dataset_/29230049
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Tang, Kankan; Zhang, Lin (2025). Comparison results on the Diabetes dataset.. PLOS ONE. Dataset
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：Comparison results on the Diabetes dataset.
- DOI：10.1371/journal.pone.0325272.t011
- 发布日期：2025-06-03
- 文件数：1 个
- 文件总体积：9.73 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Comparison_results_on_the_Diabetes_dataset_/29230049
命令行下载
curl -L -o "Table 11.xls" "https://ndownloader.figshare.com/files/55093718"
文件清单
Table 11.xls（9.73 KB）
文件总体积 9.73 KB（源站 API 实测）。
数据说明
Swarm intelligence optimization algorithms represent a significant branch of nature-inspired computational methods, designed to solve complex optimization problems by simulating the collective behavior of biological systems. Whale optimization algorithm (WOA) is a newly developed meta-heuristic algorithm, which is mainly based on the predation behavior of humpback whales in the ocean. This study proposes an enhanced version of the WOA, named the Outpost-based Multi-population Whale Optimization Algorithm (OMWOA), which integrates two key mechanisms: the outpost mechanism and a multi-population enhanced mechanism. These modifications aim to improve the algorithm’s performance in terms of solution accuracy and convergence rate. The effectiveness of OMWOA is thoroughly evaluated by benchmarking it against state-of-the-art evolutionary algorithms from the IEEE CEC 2017 and IEEE CEC 2022 competitions. Additionally, this study provides a detailed analysis of the influence of the outpost and multi-population mechanisms on OMWOA’s performance, as well as its scalability in problems of varying dimensionalities. To validate its applicability in real-world problems, the proposed algorithm is combined with Kernel Extreme Learning Machine (KELM) for solving medical disease diagnosis tasks. The experimental results demonstrate the superior performance of OMWOA in terms of diagnostic accuracy across five medical datasets, highlighting its potential for real-world applications.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0325272.t011（source: https://plos.figshare.com/articles/dataset/Comparison_results_on_the_Diabetes_dataset_/29230049）。
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