基于混合相似关系的相互信息,用于在直观模糊粗略框架及其应用中的特征选择
Anoop Kumar Tiwari1, Rajat Saini2, Abhigyan Nath3
1Department of Computer Science and Information Technology, Central University of Haryana, Mahendergarh, 123031, India.
Scientific reports
|March 13, 2024
概括
本研究引入了一种新的直觉模糊 (IF) 相互信息方法,用于特征选择. 它有效地处理混合数据中的噪声和不确定性,提高了对脂化阳性分子的预测准确性.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 模糊的集合理论 模糊的集合理论
背景情况:
- 模糊的粗和相互信息用于不确定数据集中的特征选择.
- 现有的方法在混合数据中与同时的噪音,不确定性和模糊性作斗争,降低了性能.
- 这种局限性需要更强大的方法来选择特征.
研究的目的:
- 介绍一个新的直觉模糊 (IF) 辅助的相互信息概念,用于特征选择.
- 开发一个能够处理噪音,不确定性和模糊性的IF颗粒结构.
- 提高混合值数据集上的学习算法的性能.
主要方法:
- 引入了混合IF相似性关系和相关的IF颗粒结构.
- 建立了IF粗略的条件和联合输入,并讨论了相关的相互信息.
- 证明了数学定理来验证拟议的IF概念的特征选择.
- 使用IF相互信息来删除无关/冗余特征的计算特征子集重要性.
主要成果:
- 拟议的方法有效地处理名义和混合数据中的噪声和不确定性.
- 对基准数据集的实验评估证明了该技术的实际验证和有效性.
- 在预测脂化阳性分子方面取得了高性能指标,RF(h2o) 获得了90.1%的准确性和0.922 AUC.
结论:
- 新型的IF辅助相互信息方法为复杂数据集中的特征选择提供了强大的解决方案.
- 与以前的技术相比,这种方法显著提高了处理噪音和不确定性的能力.
- 在预测脂症阳性分子中的应用凸显了拟议方法的实际实用性和有效性.
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