在不完整的顺序决策系统中基于类的属性缩小
Chunyuan Chen1,2, Feng Yin3,4, LiangKun Wu1
1School of Computer Science and Artificial Intelligence, Southwest Minzu University, Chengdu, 610041, China.
Scientific reports
|August 30, 2025
概括
这项研究引入了在不完整的有序决策系统中减少属性的新方法,提高了效率和准确性. HANDR算法增强了使用不完整数据的多标准决策.
科学领域:
- 数据科学
- 人工智能
- 机器学习
背景情况:
- 基于主导地位的粗略设置方法 (DRSA) 广泛用于采用优先顺序数据的多标准决策.
- 由于信息不完整,现实数据经常带来挑战,需要强大的属性减少技术.
- 在不完整的有序决策系统 (IODS) 中,传统的属性评估方法可能是昂贵的计算,并且可能会忽视分类的影响.
研究的目的:
- 为不完整的有序决策系统 (IODS) 提出新的属性减少方法.
- 开发一种新的属性重要性评估标准,以解决现有方法的局限性.
- 提高属性减少算法的效率和分类准确性.
主要方法:
- 在IODS中定义了类间近距离和整个类间非主导概念.
- 开发了一种使用扩展主导矩阵计算类间非主导的方法.
- 基于类间非主导 (HANDR) 算法设计了启发式属性缩小,并结合了启发式搜索策略.
主要成果:
- 与传统方法相比,提出的HANDR算法显示了运行时间的显著改善.
- 实验显示使用HANDR算法对UCI数据集的分类精度显著增加.
- 整个类间非主导性被证明是IODS中属性重要性的一个有效标准.
结论:
- 在不完整的有序决策系统中,HANDR算法提供了一个高效和准确的解决方案.
- 拟议的评估标准,即整体阶级间非主导性,有效地解决了决策中不完整信息的挑战.
- 这项研究有助于推进复杂的现实数据集的多标准决策技术.
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