组合突变和多种群驱动的差异性进化,用于数值优化和乳腺癌特征选择
Shubham Gupta1, Balkrishna Dwivedi1, Vinay Kumar2
1Department of Mathematics, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, 211004, India.
Computers in biology and medicine
|June 15, 2025
概括
本研究引入了一种先进的微分进化算法 (EMMDE),用于在高维的乳腺癌数据集中进行最佳特征选择. EMMDE提高了预测模型的效率和准确性,优于现有的方法.
科学领域:
- 计算生物学 计算生物学
- 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:
- 乳腺癌诊断受到高维数据集的挑战,降低了预测模型的效率.
- 有效的特征选择对于提高诊断准确性和模型性能至关重要.
研究的目的:
- 开发一种先进的差异进化算法 (EMMDE),用于乳腺癌诊断中的最佳特征选择.
- 通过解决高维度医疗数据的挑战,提高预测模型的效率和准确性.
主要方法:
- 开发了一种先进的差异进化算法,名为集合突变和多种群驱动的差异进化 (EMMDE).
- 在EMMDE中包含了种群划分,集合突变规则和时间变化的几何多样化方案.
- 利用高斯分类作为指导向量来平衡勘探和开发,促进融合和人口多样性.
- 开发了EMMDE的二进制版本,使用转移函数对基准和乳腺癌数据集进行验证.
主要成果:
- EMMDE 在 IEEE CEC2017 基准函数上表现出卓越的搜索能力.
- 二元EMMDE算法在UCI和乳腺癌数据集上的特征选择中取得了高度准确和有希望的结果.
- 使用MCE和其他指标的实验验证证了算法的有效性.
结论:
- 拟议的EMMDE算法显著改善了乳腺癌诊断的特征选择.
- 对于处理高维数据集和提高预测模型性能,EMMDE提供了一个强大的解决方案.
- 该研究强调了新策略对优化和特征选择问题的metaheuristics的影响.
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