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Incorporating spatial association into statistical classifiers: local pattern-based prior tuning

Authors:	Hexiang Bai Feng Cao M Peter Atkinson Qian Chen Jinfeng Wang

Institution:	1. School of Computer and Information Technology, Shanxi University , Taiyuan, Shanxi, China;2. Engineering Building, Lancaster University , Lancaster, UK https://orcid.org/0000-0002-5489-6880;3. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences , Beijing, China

Abstract:	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.

Keywords:	Spatial pattern statistical classifier spatial auto-logistic regression spatial data

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