分散差异性饥饿游戏搜索高维基因数据特征选择特征选择
Zhiqing Chen1, Li Xinxian2, Ran Guo3
1School of Intelligent Manufacturing, Wenzhou Polytechnic, Wenzhou, 325035, China.
Computers in biology and medicine
|June 30, 2023
概括
这项研究引入了DDHGS,这是一种用于基因选择的新算法,有效地减少了数据维度,并提高了生物和医学数据的分类准确性. bDDHGS方法提供了增强的功能选择性能.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 高维基遗传数据在临床决策中带来了挑战.
- 减小尺寸对于高效的数据处理和准确的分类至关重要.
- 现有的基因选择方法可能会与探索和利用之间的平衡作斗争.
研究的目的:
- 提出一种新的包装基因选择算法DDHGS,以应对高维基遗传数据的挑战.
- 在优化中增强勘探和开发之间的搜索平衡.
- 提高分类准确度,降低生物数据分析中的计算成本.
主要方法:
- 开发了分散食策略和差异进化组合HGS (DDHGS) 算法.
- 引入了一个二进制衍生品,bDDHGS,用于特征选择.
- 在IEEE CEC 2017和2014基准套件上对经典和高级算法进行了DDHGS/bDDHGS的评估.
- 在14个UCI存储库特征选择数据集上测试了bDDHGS.
主要成果:
- 在14个特征选择数据集上,bDDHGS显著超过了现有方法,包括bHGS.
- 在分类准确度,功能数量减少和更好的健身得分方面表现出明显的改进.
- 与其他算法相比,展示了较短的执行时间.
- bDDHGS在流行的优化功能和基准测试套件中表现出卓越的性能.
结论:
- bDDHGS 是一个有效的功能选择工具在包装模式.
- 拟议的DDHGS算法代表了对高维生物数据的最佳优化器.
- 这种方法提高了医学和生物学数据驱动决策的效率和准确性.
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