贪的算法用于从决策树组合中导出决策规则
Evans Teiko Tetteh1, Beata Zielosko1
1Institute of Computer Science, University of Silesia in Katowice, Bȩdzińska 39, 41-200 Sosnowiec, Poland.
Entropy (Basel, Switzerland)
|January 24, 2025
概括
本研究介绍了一种贪的算法,用于从决策树集合中提取决策规则,提高分布式数据设置中的模型解释性和准确性. 该方法通过生成更短,更准确的规则来增强知识发现.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 决策规则为知识提取和决策提供了透明度.
- 传统的算法,如CART和ID3,从数据中构建决策树.
研究的目的:
- 引入一个贪的算法来从决策树合集中推导决策规则.
- 在分布式数据环境中增强可解释性和概括性.
主要方法:
- 在代表分布式数据的引导数据集上使用CART和ID3诱导决策树.
- 应用一个贪的算法来导出在多个决策树上一致的决策规则.
主要成果:
- 增加参数α (0≤α<1) 的值会导致更短的决策规则.
- 拟议的方法可以提高基于规则的模型的分类准确性.
结论:
- 贪的算法有效地从集合中提取可解释和准确的决策规则.
- 这种方法有利于在分布式环境中对知识的表示和发现.
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