Knowledge discovery from soil maps using inductive learning
Knowledge discovery from soil maps using inductive learning
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DOI：
10.1080/13658810310001596049
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发表时间：
2003-12
影响因子：
5.7
通讯作者：
Feng Qi;A. Zhu
中科院分区：
文献类型：
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作者：
Feng Qi;A. Zhu
本文开发了一种从土壤图中提取土壤-景观模型知识的知识发现过程。它与从其他自然资源地图中发现知识具有广泛的相关性。该过程包括四个主要步骤：数据准备、数据预处理、模式提取和知识整合。为了从容易出错的土壤图中恢复真正的专家知识，我们的研究特别关注土壤图中表示噪声的降低。数据前处理步骤在获得更高的准确性方面发挥了重要作用。一种基于环境直方图模式的像素采样方法在降低噪声和构造具有代表性的样本集方面被证明是有效的。比较了See5决策树算法、朴素贝叶斯算法和人工神经网络三种归纳学习算法的学习精度和结果可理解性。见5被证明是一种准确的方法，并产生最容易理解的结果，这与制作土壤图所使用的规则(专家知识)是一致的。将空间信息融入到知识发现过程中，不仅提高了提取知识的准确性，而且增加了提取的土壤-景观模型的显性和广泛性。
This paper develops a knowledge discovery procedure for extracting knowledge of soil-landscape models from a soil map. It has broad relevance to knowledge discovery from other natural resource maps. The procedure consists of four major steps: data preparation, data preprocessing, pattern extraction, and knowledge consolidation. In order to recover true expert knowledge from the error-prone soil maps, our study pays specific attention to the reduction of representation noise in soil maps. The data preprocessing step has exhibited an important role in obtaining greater accuracy. A specific method for sampling pixels based on modes of environmental histograms has proven to be effective in terms of reducing noise and constructing representative sample sets. Three inductive learning algorithms, the See5 decision tree algorithm, Naïve Bayes, and artificial neural network, are investigated for a comparison concerning learning accuracy and result comprehensibility. See5 proves to be an accurate method and produces the most comprehensible results, which are consistent with the rules (expert knowledge) used in producing the soil map. The incorporation of spatial information into the knowledge discovery process is found not only to improve the accuracy of the extracted knowledge, but also to add to the explicitness and extensiveness of the extracted soil-landscape model.