ABSTRACT This paper proposes a new classification method for spatial data by adjusting prior class probabilities according to local spatial patterns. First, the proposed method uses a classical statistical classifier to model training data. Second, the prior class probabilities are estimated according to the local spatial pattern and the classifier for each unseen object is adapted using the estimated prior probability. Finally, each unseen object is classified using its adapted classifier. Because the new method can be coupled with both generative and discriminant statistical classifiers, it performs generally more accurately than other methods for a variety of different spatial datasets. Experimental results show that this method has a lower prediction error than statistical classifiers that take no spatial information into account. Moreover, in the experiments, the new method also outperforms spatial auto-logistic regression and Markov random field-based methods when an appropriate estimate of local prior class distribution is used.

中文翻译：

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将空间关联纳入统计分类器：基于局部模式的先验调整

摘要 本文通过根据局部空间模式调整先验类概率，提出了一种新的空间数据分类方法。首先，所提出的方法使用经典的统计分类器对训练数据进行建模。其次，根据局部空间模式估计先验类概率，并使用估计的先验概率调整每个看不见的对象的分类器。最后，每个看不见的对象都使用其适应的分类器进行分类。由于新方法可以与生成和判别统计分类器结合使用，因此对于各种不同的空间数据集，它通常比其他方法执行得更准确。实验结果表明，与不考虑空间信息的统计分类器相比，该方法具有更低的预测误差。而且，