关于模糊知识库系统的可解释性
Francesco Camastra1, Angelo Ciaramella1, Giuseppe Salvi2
1Dipartimento di Scienze e Tecnologie, Università degli Studi di Napoli Parthenope, Naples, Italy.
PeerJ. Computer science
|December 9, 2024
概括
这项研究引入了一种新的算法,以尽量减少模糊的规则基础,提高人工智能系统的可解释性. 该方法使用粗略的集合理论,简化模糊的规则,以获得更好的决策支持和推系统.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据挖掘 数据挖掘
背景情况:
- 模糊的基于规则的系统越来越多地用于可解释和解释的AI (XAI) 作为预先方法.
- 虽然这些系统提供了人类可以理解的知识表示,但保持简单性和规则基础紧性对于真正的可解释性至关重要.
研究的目的:
- 提出一个有效的算法,以尽量减少模糊的规则基础.
- 为了提高模糊的基于规则的系统的可解释性和紧性,用于实际应用.
主要方法:
- 拟议的算法利用粗略的集合理论与贪的策略相结合.
- 它的重点是减少规则基础中的模糊规则的数量.
主要成果:
- 最小化算法成功地简化了模糊的规则基础.
- 使用真实和基准数据集的验证显示了令人鼓舞的性能改进.
结论:
- 开发的算法有助于构建更易于解释的推理系统.
- 这种简化对于诸如决策支持和推系统等应用是有益的.
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