一个序列决策理论粗略集合模型的新概括及其应用
Tanzeela Shaheen1, Hamrah Batool Khan1, Wajid Ali1
1Department of Mathematics, Air University, PAF Complex E-9 Islamabad, 44230, Pakistan.
Heliyon
|July 23, 2024
概括
本研究介绍了通用序列决策理论粗略集 (GSeq-DTRS),这是一个先进的数据分析方法. GSeq-DTRS通过高效处理复杂数据集和减少代而无需降低属性来改善决策.
科学领域:
- * 人工智能 * 人工智能
- * 数据挖掘 * 数据挖掘
- * 机器学习 * 机器学习
背景情况:
- * 传统的决策理论粗略集 (DTRS) 在序列处理和属性集成方面存在局限性.
- * 现有的方法经常与连续规模的数据集扎,需要降低属性.
- *三方决策 (3WD) 方法为细微的决策提供了一个框架.
研究的目的:
- * 引入通用顺序决策理论粗略集 (GSeq-DTRS),这是一个增强的DTRS模型.
- * 将三向决策 (3WD) 方法纳入多层次边界区域勘探.
- * 开发一种可靠的方法来处理连续和离散的数据集,而无需降低属性.
主要方法:
- * GSeq-DTRS的开发,包括一般化的颗粒和相似性/耐受性关系.
- *使用一般化颗粒结构 (GGS) 进行多层次分类.
- * 条件概率 (CP) 的改进与容忍类和算法设计保持一致.
主要成果:
- * GSeq-DTRS有效地将元素分为正 (POS) 或负 (NEG) 区域,用于连续和离散数据.
- *该方法可以处理多级分类,而无需在每个阶段减少属性.
- *实验分析表明,与传统方法相比,对参数值的敏感性较低,代次数较少.
结论:
- * GSeq-DTRS在决策理论大致集合理论和三向决策方面取得了重大进展.
- * 该方法为复杂的数据分类任务提供了高效和有效的解决方案.
- *GSeq-DTRS具有明显的潜力,可以在各种领域加强现实世界的决策过程.
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