IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remot

DOI: 10.1109/igarss.2000.860340

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Analytical decision boundary feature extraction for neural networks

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A novel feature ranking modelling in GIS context: Addressing complexity and cost issues

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2009

2009 17th International Conference on Geoinformatics

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nowadays, there is the trend to carry out decisions and analysis on geospatial data by a massive computational approach. The amount of geospatial information available is increasing exponentially as result of the increasing interoperability between informative systems. In a multiplicity of applications and services spatial decision is carried out to pursue business goals, often without involving experts in geography. The informative systems have an increasing autonomous decisional capability on information selection and analysis. The demand is to have systems that require only an input goal, and produces decisions that humans can understand and integrate with their own decisions. In this paper it is proposed an automatic method of feature ranking, which can sort a heterogeneous set of features by their importance in accomplishing an analytical goal. This method produces a rank model that helps to select the minimal set of features needed to pursue a goal with a wanted accuracy or resources involvement. This feature ranking is expected to supports fundamental decisional making in elaborating geospatial data. The method is based on data mining algorithms; the obtained rank model appears to be spatially scalable and fits well to human form of knowledge.

A novel feature ranking modelling in GIS context: Addressing complexity and cost issues

¹

,

²

,

³

2009

2009 17th International Conference on Geoinformatics

View full text Add to dashboard Cite

nowadays, there is the trend to carry out decisions and analysis on geospatial data by a massive computational approach. The amount of geospatial information available is increasing exponentially as result of the increasing interoperability between informative systems. In a multiplicity of applications and services spatial decision is carried out to pursue business goals, often without involving experts in geography. The informative systems have an increasing autonomous decisional capability on information selection and analysis. The demand is to have systems that require only an input goal, and produces decisions that humans can understand and integrate with their own decisions. In this paper it is proposed an automatic method of feature ranking, which can sort a heterogeneous set of features by their importance in accomplishing an analytical goal. This method produces a rank model that helps to select the minimal set of features needed to pursue a goal with a wanted accuracy or resources involvement. This feature ranking is expected to supports fundamental decisional making in elaborating geospatial data. The method is based on data mining algorithms; the obtained rank model appears to be spatially scalable and fits well to human form of knowledge.

Analytical decision boundary feature extraction for neural networks for the recognition of unconstrained handwritten digits

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Lee²

SMC 2000 Conference Proceedings. 2000 IEEE International Conference on Systems, Man and Cybernetics. 'Cybernetics Evolving to S

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Feature extraction for deep neural networks based on decision boundaries

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2017

SPIE Proceedings

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