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  • Search: subject:"Data resampling"
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Year of publication
Subject
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Data resampling 3 Artificial intelligence 2 Forecasting model 2 Künstliche Intelligenz 2 Prognoseverfahren 2 conditional bias 2 conditionally biased estimation 2 cross validation 2 data resampling 2 differential gene expression 2 empirical Bayes 2 feature selection bias 2 fold change estimation 2 gene rank 2 multiple comparisons 2 shrinkage 2 transcriptional microarray 2 ANOVA/MANOVA 1 Analytics 1 Bayesian weights 1 Big Data 1 Big data 1 Data Mining 1 Data mining 1 Data quality 1 Datenqualität 1 EEG 1 Feuer 1 Fire 1 Imbalanced regression 1 Insolvency 1 Insolvenz 1 Linear time-series analysis 1 Machine learning 1 Nonlinear time-series analysis 1 Oversampling 1 Predictive analytics 1 Propensity score matching 1 Regression analysis 1 Regressionsanalyse 1
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Online availability
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Undetermined 5 CC license 1 Free 1
Type of publication
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Article 6
Type of publication (narrower categories)
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Article in journal 3 Aufsatz in Zeitschrift 3
Language
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English 3 Undetermined 3
Author
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Bickel, David 2 Bacao, Fernando 1 Camacho, Luís 1 Catry, Filipe X. 1 Chen, Yi-Hsien 1 Dressel, M. 1 Dünki, R.M. 1 Gennaro, Franco 1 Lin, Wei-Chao 1 Maldonado, Sebastián 1 Tsai, Chih-Fong 1 Vairetti, Carla 1
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Published in...
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Statistical Applications in Genetics and Molecular Biology 2 Decision analytics journal 1 European journal of operational research : EJOR 1 Journal of forecasting 1 Physica A: Statistical Mechanics and its Applications 1
Source
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ECONIS (ZBW) 3 RePEc 3
Showing 1 - 6 of 6
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An integrated resampling and machine learning framework for predictive analytics of large wildfires
Camacho, Luís; Catry, Filipe X.; Bacao, Fernando - In: Decision analytics journal 18 (2026), pp. 1-9
Accurately predicting the final burned area of wildfires at the moment of ignition is a challenging problem with important implications for disaster management and resource allocation. In this study, we apply recently developed resampling techniques, combined with machine learning algorithms, to...
Persistent link: https://www.econbiz.de/10015652281
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Data quality improvement for financial distress prediction : feature selection, data re-sampling, and their combinations in different orders
Tsai, Chih-Fong; Lin, Wei-Chao; Chen, Yi-Hsien - In: Journal of forecasting 44 (2025) 7, pp. 2205-2229
Persistent link: https://www.econbiz.de/10015464785
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Propensity score oversampling and matching for uplift modeling
Vairetti, Carla; Gennaro, Franco; Maldonado, Sebastián - In: European journal of operational research : EJOR 316 (2024) 3, pp. 1058-1069
Persistent link: https://www.econbiz.de/10014577753
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Correcting the Estimated Level of Differential Expression for Gene Selection Bias: Application to a Microarray Study
Bickel, David - In: Statistical Applications in Genetics and Molecular Biology 7 (2009) 1, pp. 10-10
The level of differential gene expression may be defined as a fold change, a frequency of upregulation, or some other measure of the degree or extent of a difference in expression across groups of interest. On the basis of expression data for hundreds or thousands of genes, inferring which genes...
Persistent link: https://www.econbiz.de/10005046588
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Correcting the Estimated Level of Differential Expression for Gene Selection Bias: Application to a Microarray Study
Bickel, David - In: Statistical Applications in Genetics and Molecular Biology 7 (2008) 1, pp. 10-10
The level of differential gene expression may be defined as a fold change, a frequency of upregulation, or some other measure of the degree or extent of a difference in expression across groups of interest. On the basis of expression data for hundreds or thousands of genes, inferring which genes...
Persistent link: https://www.econbiz.de/10005585070
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Statistics of biophysical signal characteristics and state specificity of the human EEG
Dünki, R.M.; Dressel, M. - In: Physica A: Statistical Mechanics and its Applications 370 (2006) 2, pp. 632-650
statistics. As a step towards such a statistical assessment we present here a data resampling approach: Data resampling …
Persistent link: https://www.econbiz.de/10011057038
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