On the Application of Inductive Machine Learning Tools to Geographical Analysis
On the Application of Inductive Machine Learning Tools to Geographical Analysis
复制标题
DOI：
10.1111/j.1538-4632.2000.tb00420.x
复制
发表时间：
2010-09
影响因子：
3.6
通讯作者：
M. Gahegan
中科院分区：
文献类型：
--
作者：
M. Gahegan
归纳性机器学习工具，例如神经网络和决策树，提供了分类、聚类和模式识别的替代方法，理论上可以扩展到遍布地理的复杂或“深层”数据集。相比之下，由于可扩展性和灵活性问题，传统的统计方法可能会失败。本文讨论了归纳机器学习在地理分析中的作用。所提出的讨论不是基于比较结果或数学描述，而是侧重于各种归纳学习方法在操作上不同的微妙方式，描述了（1）特征空间的分区或聚类方式，（2）用于识别良好解决方案的搜索机制，以及（3）每种技术施加的不同偏差。然后详细描述了在考虑复杂的地理特征空间时这些问题所产生的后果。总体目标是提供构建可靠的归纳分析方法的基础，而不是依赖于归纳学习工具所需的各种操作标准的零碎或随意的实验。通常，地理领域的从业者似乎并没有很好地理解这些标准，这可能导致配置和操作困难，并最终导致性能不佳。
Inductive machine learning tools, such as neural networks and decision trees, offer alternative methods for classification, clustering, and pattern recognition that can, in theory, extend to the complex or “deep” data sets that pervade geography. By contrast, traditional statistical approaches may fail, due to issues of scalability and flexibility. This paper discusses the role of inductive machine learning as it relates to geographical analysis. The discussion presented is not based on comparative results or on mathematical description, but instead focuses on the often subtle ways in which the various inductive learning approaches differ operationally, describing (1) the manner in which the feature space is partitioned or clustered, (2) the search mechanisms employed to identify good solutions, and (3) the different biases that each technique imposes. The consequences arising from these issues, when considering complex geographic feature spaces, are then described in detail. The overall aim is to provide a foundation upon which reliable inductive analysis methods can be constructed, instead of depending on piecemeal or haphazard experimentation with the various operational criteria that inductive learning tools call for. Often, it would appear that these criteria are not well understood by practitioners in the geographic sphere, which can lead to difficulties in configuration and operation, and ultimately to poor performance.