Classification by ensembles from random partitions of high-dimensional data
From MaRDI portal
Publication:1020719
DOI10.1016/J.CSDA.2006.12.043zbMath1203.62115OpenAlexW2105875820MaRDI QIDQ1020719
Hojin Moon, Ralph L. Kodell, James J. Chen, Noha Lim, Melissa J. Fazzari, Hongshik Ahn
Publication date: 2 June 2009
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2006.12.043
Related Items (5)
A new ensemble method with feature space partitioning for high-dimensional data classification ⋮ High-dimensional Canonical Forest ⋮ Unnamed Item ⋮ Taxonomy for characterizing ensemble methods in classification tasks: a review and annotated bibliography ⋮ Canonical forest
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Bagging predictors
- A decision-theoretic generalization of on-line learning and an application to boosting
- Limits on the majority vote accuracy in classifier fusion
- Arcing classifiers. (With discussion)
- Binary Regression Using an Extended Beta-Binomial Distribution, With Discussion of Correlation Induced by Covariate Measurement Errors
- 394: The Analysis of Binary Responses from Toxicological Experiments Involving Reproduction and Teratogenicity
- Tree-Structured Logistic Model for Over-Dispersed Binomial Data with Application to Modeling Developmental Effects
- Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
- Selection bias in gene extraction on the basis of microarray gene-expression data
- Model Selection and Multimodel Inference
- The elements of statistical learning. Data mining, inference, and prediction
- Random forests
- A new look at the statistical model identification
This page was built for publication: Classification by ensembles from random partitions of high-dimensional data
