Meta-Interpretive Learning of Data Transformation Programs
Meta-Interpretive Learning of Data Transformation Programs
复制标题
数据转换程序的元解释学习
DOI：
10.1007/978-3-319-40566-7_4
复制
发表时间：
2015
影响因子：
1.4
通讯作者：
S. Muggleton
中科院分区：
文献类型：
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作者：
Andrew Cropper;Alireza Tamaddoni;S. Muggleton
数据转换涉及大量专用程序的手动构建。尽管通常很小，但此类程序可能很复杂，涉及问题分解，递归和对上下文的认可。在商业和学术数据分析项目中构建此类程序很常见，并且可以是劳动密集型且昂贵的，使其成为机器学习的合适候选人。在本文中，我们使用元解释学习框架（MIL）从少量示例中学习递归数据转换程序。 MIL非常适合这项任务，因为它通过谓词发明，学习递归程序，从少数示例中学习以及仅从积极的例子中学习来支持问题分解。我们将MEL的实施物应用于半结构化和非结构化数据。我们在三个现实世界数据集上进行实验：医疗患者记录，XML卫生记录和自然语言从生态文件中获取。实验结果表明，在这些任务中可以通过少量培训示例实现高水平的预测精度，尤其是在递归学习时。
Data transformation involves the manual construction of large numbers of special-purpose programs. Although typically small, such programs can be complex, involving problem decomposition, recursion, and recognition of context. Building such programs is common in commercial and academic data analytic projects and can be labour intensive and expensive, making it a suitable candidate for machine learning. In this paper, we use the meta-interpretive learning framework (MIL) to learn recursive data transformation programs from small numbers of examples. MIL is well suited to this task because it supports problem decomposition through predicate invention, learning recursive programs, learning from few examples, and learning from only positive examples. We apply Metagol, a MIL implementation, to both semi-structured and unstructured data. We conduct experiments on three real-world datasets: medical patient records, XML mondial records, and natural language taken from ecological papers. The experimental results suggest that high levels of predictive accuracy can be achieved in these tasks from small numbers of training examples, especially when learning with recursion.
DOI：
--
发表时间：
1999-08
期刊：
Proceedings. International Conference on Intelligent Systems for Molecular Biology
影响因子：
--
作者：
M. Craven;J. Kumlien
通讯作者：
M. Craven;J. Kumlien