综述利用图形数据结构和知识图表的特征选择策略
Sisi Shao1, Pedro Henrique Ribeiro2, Christina M Ramirez1
1Department of Biostatistics, Fielding School of Public Health at University of California, Los Angeles, 650 Charles E Young Dr S, Los Angeles, CA 90095-1772, United States.
Briefings in bioinformatics
|November 11, 2024
概括
知识图 (KG) 中的特征选择增强了机器学习模型,以获得更好的洞察力. 本次审查强调了可扩展性,准确性和可解释性是推动KG特征选择 (FS) 和其应用的关键.
科学领域:
- 计算机科学 计算机科学
- 生物信息学是一种生物信息学.
- 人工智能的人工智能
背景情况:
- 在生物医学研究和NLP等领域的知识图 (KG) 中,特征选择 (FS) 是至关重要的.
- 基因基因增强机器学习 (ML) 模型的有效性,假设生成和可解释性.
研究的目的:
- 审查知识图中特征选择的方法.
- 强调FS在提高ML模型性能和可解释性方面的作用.
- 为了催化KG的创新,为各种应用选择功能.
主要方法:
- 对KG的现有特征选择技术的全面审查.
- 分析关键因素,包括可扩展性,准确性和可解释性.
- 探索领域知识整合和多目标优化.
主要成果:
- 确定了可扩展性,准确性和可解释性对KG FS至关重要.
- 强调了整合领域知识的潜力,以完善FS.
- 展示了多目标优化和跨学科合作的影响.
结论:
- 特性选择对于推进基于KG的分析模型至关重要.
- 未来的方向包括可扩展,动态的FS算法和可解释的AI集成.
- 基因组特征选择对精准医学和其他领域具有变革潜力.
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