NSICA:在心律失常诊断中选择特征的多目标帝国主义竞争算法
Mehdi Ayar1, Ayaz Isazadeh1, Farhad Soleimanian Gharehchopogh1
1Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran.
Computers in biology and medicine
|May 28, 2023
概括
这项研究引入了一种用于最佳特征选择的新算法,增强了心律失常诊断. 非主导,帝国主义竞争算法 (NSICA) 提高了准确性,并有效地减少了特征.
科学领域:
- 计算智能是一种计算智能.
- 机器学习 机器学习
- 生物信息学是一种生物信息学.
背景情况:
- 最佳的特征选择对于有效的机器学习模型性能至关重要.
- 现有的算法经常与多目标优化和离散化挑战作斗争.
- 准确的心律失常诊断需要从复杂的数据集中进行强大的特征选择.
研究的目的:
- 提出一个新的多目标,非主导,帝国主义竞争算法 (NSICA) 进行最佳的特征选择.
- 通过修改的ICA操作和非主导分类来解决特征选择中的离散和精英主义问题.
- 通过使用心电图数据,评估NSICA在诊断心律不整方面的有效性.
主要方法:
- 开发了一个名为NSICA的帝国主义竞争算法 (ICA) 的多目标和离散版本.
- 纳入非主导分类和修改ICA运营以处理隐私化和精英主义.
- 应用NSICA作为基于心电图的心律失常分类的特征选择系统.
主要成果:
- 通过NSICA有效地确定了帕雷托最佳特征,用于心律失常的分类.
- 该算法实现了高准确性,同时最大限度地减少了所选特征的数量和虚假消极性.
- 对比评估表明NSICA的性能优于最先进的算法.
结论:
- 对于多目标的最佳特征选择问题,NSICA是一种高效和可适应的算法.
- 拟议的方法显着有望提高心律失常诊断的准确性和效率.
- NSICA提供了一个可定制的解决方案,适用于各种特征选择挑战.
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