一个新的群体初始化基于混合模糊粗略集的元启发算法,用于高维基因数据的特征选择
Xuanming Guo1, Jiao Hu1, Helong Yu2
1College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
Computers in biology and medicine
|October 19, 2023
概括
这项研究引入了一种用于高维特征选择的新型混合方法,将模糊的粗略集与元启发学相结合. 该方法通过使用粗略的集合属性进行元启发来显著提高特征选择算法的性能.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 数据科学数据科学数据科学
背景情况:
- 高维基遗传数据带来了重要的处理和尺寸挑战.
- 识别关键基因对于减少数据维度至关重要.
- 传统的过方法如MRMR和ReliefF的性能有限.
研究的目的:
- 为高维特征选择提出一种新的过器-包装器混合方法.
- 为了利用模糊的粗略设置方法来减少属性和元启发.
- 为了提高特征选择中的元启发算法的性能.
主要方法:
- 利用基于混合模糊粗集的二进制鱼优化算法 (bWOA) 的变体来减少属性.
- 雇佣了减少的属性作为先前知识来初始化人口的元启发算法.
- 在14个UCI数据集上使用5个算法进行实验.
主要成果:
- 提出的初始化方法显著提高了五个增强算法的性能.
- 改进的bMFO (fuzzy_bMFO) 超过了六个先进的算法.
- 证明了初始化方法对于高维特征选择的适用性.
结论:
- 这种新的过器包裹混合方法有效地解决了高维数据中的挑战.
- 提出的初始化策略提高了特征选择中的元启发算法性能.
- 这种方法为基因选择和数据维度减少提供了一个有前途的解决方案.
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