Performance assessment of artificial neural network using chi-square and backward elimination featur...
Performance assessment of artificial neural network using chi-square and backward elimination feature selection methods for landslide susceptibility analysis
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Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
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Language
English
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Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
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In the machine learning models, it is desirable to remove most redundant features from the data set to reduce the data processing time and to improve accuracy of the models. In this paper, chi-square (CS) and backward elimination (BE), which are well-known feature selection methods, were used for the optimum selection of input features/factors for...
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Full title
Performance assessment of artificial neural network using chi-square and backward elimination feature selection methods for landslide susceptibility analysis
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TN_cdi_proquest_journals_2579207507
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2579207507
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ISSN
1866-6280
E-ISSN
1866-6299
DOI
10.1007/s12665-021-09998-5
