一种混合特征选择算法,结合了信息获取和分组粒子群的优化,用于癌症诊断
Fangyuan Yang1, Zhaozhao Xu2, Hong Wang1
1Department of Gynecologic Oncology, The First Affiliated Hospital of Henan Polytechnic University, Jiaozuo, Henan, China.
PloS one
|March 11, 2024
概括
这项研究引入了一种新的混合特征选择方法,即信息获取和分组粒子集群优化 (IG-GPSO),以增强基于机器学习的癌症诊断. IG-GPSO算法显著提高了支持矢量机 (SVM) 模型的准确性.
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.
- 机器学习在医学中的应用
背景情况:
- 机器学习越来越多地应用于癌症诊断.
- 支持向量机 (SVM) 对于高维,小样本数据是有效的.
- 基因表达数据的高维度和冗余性挑战了SVM的性能.
研究的目的:
- 用基因表达数据来解决癌症诊断中的SVM限制.
- 提出一种混合特征选择算法,以提高分类准确度.
主要方法:
- 开发了一种混合特征选择算法,即信息获取和分组粒子集群优化 (IG-GPSO).
- 功能根据信息获取排名,然后分组.
- 用于特征子集选择的方法是分组粒子集群优化 (GPSO).
主要成果:
- 对于SVM,IG-GPSO算法实现了98.50%的平均准确性 (ACC).
- 这种精度明显超过了传统的特征选择方法.
- 选择的特征子集与KNN相比显示出最佳分类.
结论:
- IG-GPSO算法产生了卓越的分类效果和最小特征尺度 (FS).
- IG-GPSO 显著提高了 SVM 在癌症诊断中的准确性.
- 这种方法为改进基于机器学习的癌症诊断提供了有前途的方法.
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