高阶碎形信念 Rényi分歧与其在模式分类中的应用
IEEE transactions on pattern analysis and machine intelligence
|August 31, 2023
概括
本研究引入了一种新的方法来测量不确定信息中的差异,使用动态分数概率转换和更高阶分数信念雷尼分歧 (HOFBReD). 拟议的基于HOFBReD的融合算法提高了多源信息处理中的模式识别准确性.
科学领域:
- 不确定性定量化 不确定性定量化
- 证据理论证据理论
- 信息融合 信息融合
背景情况:
- 斯特-沙弗证据理论有效地模拟不确定的信息,但与高度相互矛盾的数据作斗争.
- 德姆斯特组合规则可以产生反直观的结果,降低决策准确性.
- 衡量冲突对于提高不确定的信息处理中的决策水平至关重要.
研究的目的:
- 提出一种用于测量证据之间差异的新方法.
- 为量化不确定性引入一个新的更高阶分数信念雷尼分歧 (HOFBReD).
- 根据拟议的差异测量,开发一个改进的多源信息融合算法.
主要方法:
- 开发了一个动态分形概率转换模型,以从基本信念赋值 (BBA) 中提取更多信息.
- 提议使用高阶分形信念雷尼分歧 (HOFBReD) 来测量BBA之间的差异,并采用动态分形概率转换.
- 一个新的多源信息融合算法是使用HOFBReD测量设计的.
主要成果:
- 霍夫布雷德有效地测量了BBA之间的差异,并拥有与概率转换和差异相关的理想性质.
- 当动态分形概率转换结束时,HOFBReD与在猪形概率转换上的雷尼分歧保持一致.
- 与现有方法相比,拟议的融合算法在现实数据集中显示出高于平均模式识别准确度的优势.
结论:
- 新的差异测量方法和多源信息融合算法有助于在处理不确定的信息时提高决策水平.
- 霍夫布雷德提供了一种强大的方法来衡量证据差异,特别是在数据冲突的情况下.
- 这些发现表明,在处理和融合不确定的信息以更好地做出决策方面取得了重大进展.
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