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Algorithms 2016, 9(3), 56; doi:10.3390/a9030056

Multiple Artificial Neural Networks with Interaction Noise for Estimation of Spatial Categorical Variables

Department of Statistics, Central South University, Changsha 410012, China
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Author to whom correspondence should be addressed.
Academic Editor: Toly Chen
Received: 25 May 2016 / Revised: 4 August 2016 / Accepted: 17 August 2016 / Published: 20 August 2016
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Abstract

This paper presents a multiple artificial neural networks (MANN) method with interaction noise for estimating the occurrence probabilities of different classes at any site in space. The MANN consists of several independent artificial neural networks, the number of which is determined by the neighbors around the target location. In the proposed algorithm, the conditional or pre-posterior (multi-point) probabilities are viewed as output nodes, which can be estimated by weighted combinations of input nodes: two-point transition probabilities. The occurrence probability of a certain class at a certain location can be easily computed by the product of output probabilities using Bayes’ theorem. Spatial interaction or redundancy information can be measured in the form of interaction noises. Prediction results show that the method of MANN with interaction noise has a higher classification accuracy than the traditional Markov chain random fields (MCRF) model and can successfully preserve small-scale features. View Full-Text
Keywords: data interaction; neural networks; spatial categorical variables; transition probabilities data interaction; neural networks; spatial categorical variables; transition probabilities
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Huang, X.; Wang, Z. Multiple Artificial Neural Networks with Interaction Noise for Estimation of Spatial Categorical Variables. Algorithms 2016, 9, 56.

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