通过相互信息分解建模分类变量
Jiun-Wei Liou1, Michelle Liou2, Philip E Cheng2
1Department of Electrical Engineering, Ming Chi University of Technology, New Taipei City 243, Taiwan.
Entropy (Basel, Switzerland)
|May 27, 2023
概括
本研究介绍了相互信息 (MI) 分解,用于识别关键变量及其在应急表中的相互作用. 这种新的方法简化了复杂的数据,有助于构建可解释的统计模型.
科学领域:
- 统计 统计 统计 统计
- 数据分析 数据分析
- 机器学习 机器学习
背景情况:
- 应急表分析通常涉及众多变量之间的复杂相互作用.
- 识别不可或缺的变量及其关系对于构建节和可解释的模型至关重要.
- 现有的方法可能会与高维或稀疏的应急表作斗争.
研究的目的:
- 提出并验证一种使用相互信息 (MI) 分解进行应急表分析的新方法.
- 识别基本变量及其相互作用,以便构建简化的统计模型.
- 将MI分解的疗效与现有的最先进方法进行比较.
主要方法:
- 相互信息 (MI) 分解被用来识别基于多项式分布的关联变量子集.
- 已识别的子集被用于验证节的日志线性和物流模型.
- 该方法在两个现实数据集上进行了评估:缺血性中风风险因素和银行信贷属性.
主要成果:
- MI分解成功地确定了关键变量子集及其相互作用.
- 该方法促进了节的日志线性和物流模型的验证.
- 经验性比较表明MI分析的有效性与其他主要的变量和模型选择方法相比.
结论:
- 相互信息 (MI) 分解为分析应急表提供了一种强大而新的方法.
- 这种方法可以从离散的多变量数据中构建简洁和可解释的统计模型.
- MI分析方案为复杂数据集中的变量和模型选择提供了一个强大的工具.
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