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Resampling in neural networks with application to spatial analysis

Geo-Spatial Information Science 2022年第3期25卷 413-424页

作者： Bruno Póvoa Rodrigues Vinicius Francisco Rofatto Marcelo Tomio Matsuoka talita teles assunção Graduate Program in Agriculture and Geospatial Information Federal University of UberlândiaMonte CarmeloBrazil Institute of Geography Federal University of UberlândiaMonte CarmeloBrazil

In developing Artificial Neural Networks(ANNs),the available dataset is split into three categories:training,validation and ***,an important problem arises:How to trust the predic-tion provided by a particular ANN?Due to the randomness related to the network itself(architecture,initialization and learning procedure),there is usually no best *** this issue,we provide a framework,which captures the randomness related to the network *** idea is to perform several training and test trials based on the Jackknife resampling *** consists of iteratively deleting a single observation each time from the sample and recomputing the ANN on the rest of the sample ***,interval prediction is available instead of point *** proposed method was applied and tested using pH,Ca and P data obtained by analyzing 118 georeferenced soil *** results,based on the dataset size simulation,showed that 60%reduction in available dataset offers compatible accuracy in relation to full dataset,and therefore a higher cost of sampling in the field would not be *** re-sampling method spatially characterizes the points of greater or lesser accuracy and *** re-sampling method increased the success rate by using interval prediction instead of using the mean as the most probable *** we restrict it to the regression neural network model,the resampling method proposed can also be extended to other modern statistical tools,such as Kriging,Least Squares Collocation(LSC),Convolutional Neural Network(CNN),and so on.

关键词： Artificial Neural Network(ANN) data splitting resampling delete-1 Jackknife spatial analysis

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