通过安全的模式修剪,为预测模式采矿模型进行有效的模型选择
Takumi Yoshida1, Hiroyuki Hanada2, Kazuya Nakagawa1
1Department of Engineering, Nagoya Institute of Technology, Nagoya, Aichi 466-8555, Japan.
Patterns (New York, N.Y.)
|December 18, 2023
概括
预测模式挖掘从结构化数据中构建模型,但面临着太多模式的挑战. 本研究介绍了一种安全的图案修剪方法,以有效地管理模型构建中的图案数.
科学领域:
- 数据挖掘和机器学习
- 计算智能是一种计算智能.
- 模式识别 模式识别
背景情况:
- 预测模式挖掘从结构化数据 (如集合,图表和序列) 中构建模型.
- 它利用子结构 (模式) 作为模型特征,面临着指数式模式增长的挑战.
- 这种增长使模型构建复杂化,并降低了效率.
研究的目的:
- 提出一种新的方法来应对在预测型模式挖矿中过多的模式数量的挑战.
- 引入安全的图案修剪方法,以实现高效的模型构建.
- 证明该方法在各种结构化数据类型和机器学习任务中的适用性.
主要方法:
- 开发了一种"安全的模式修剪"技术,以减轻模式的组合式爆炸.
- 将剪裁方法集成到预测模式采矿工作流中.
- 通过使用集合,图形和序列数据,对回归和分类任务进行数值实验.
主要成果:
- 提出的安全模式修剪方法有效地减少了模式的数量.
- 该方法在实际数据分析和模型构建中表现出效率.
- 在各种结构化数据的回归和分类问题中成功应用.
结论:
- 安全的模式修剪是预测模式采矿中模式爆炸问题的可行解决方案.
- 该方法提高了从结构化数据构建预测模型的实用性和效率.
- 这种方法为处理复杂数据类型的机器学习提供了重大进步.
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