On the fusion of threshold classifiers for categorization and dimensionality reduction
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DOI: 10.1007/s00180-011-0243-7
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References listed on IDEAS
- Ruschhaupt Markus & Huber Wolfgang & Poustka Annemarie & Mansmann Ulrich, 2004. "A Compendium to Ensure Computational Reproducibility in High-Dimensional Classification Tasks," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-26, December.
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Cited by:
- Ludwig Lausser & Florian Schmid & Lyn-Rouven Schirra & Adalbert F. X. Wilhelm & Hans A. Kestler, 2018. "Rank-based classifiers for extremely high-dimensional gene expression data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(4), pages 917-936, December.
- Yu-Chuan Chen & Hyejung Ha & Hyunjoong Kim & Hongshik Ahn, 2014. "Canonical Forest," Computational Statistics, Springer, vol. 29(3), pages 849-867, June.
- Elena Moltchanova & Myroslava Lesiv & Linda See & Julie Mugford & Steffen Fritz, 2022. "Optimizing Crowdsourced Land Use and Land Cover Data Collection: A Two-Stage Approach," Land, MDPI, vol. 11(7), pages 1-15, June.
- Markus Maucher & David Kracht & Steffen Schober & Martin Bossert & Hans Kestler, 2014. "Inferring Boolean functions via higher-order correlations," Computational Statistics, Springer, vol. 29(1), pages 97-115, February.
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Keywords
Feature reduction; Threshold classifiers; High dimensional data;All these keywords.
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