Uncovering Bivariate Interactions in High Dimensional Data Using Random Forests with Data Augmentation
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Summary
This paper proposes a search strategy that explores a subset of the input space in an exhaustive way using RF as the search engine and uses the out of bag error rate of the ensemble, obtained when trained over an augmented data set, as criterion to capture difficult to uncover bivariate patterns associated with an outcome variable.
- Type
- article
- Published
- 2011-04-01
- Cited by
- 2
- References
- 38
- OpenAlex
- https://openalex.org/W1813349946
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:44972360
Keywords
Bivariate analysis, Random forest, Bivariate data, Computer science, Data mining
References
- Use of Noise to Augment Training Data: A Neural Network Method of Mineral–Potential Mapping in Regions of Limited Known Deposit Examples
- Classification and Regression by randomForest
- Gene selection and classification of microarray data using random forest
- Classification and regression trees
- Random Forests: some methodological insights
- New feature subset selection procedures for classification of expression profiles
- Augmenting the bootstrap to analyze high dimensional genomic data
- Bootstrapping with Noise: An Effective Regularization Technique
- Detecting multivariate differentially expressed genes
- Ensemble methods and data augmentation by noise addition applied to the analysis of spectroscopic data
- Noise injection for training artificial neural networks: a comparison with weight decay and early stopping.
- Data augmentation: an alternative approach to the analysis of spectroscopic data
- Multivariate approach for selecting sets of differentially expressed genes.
- Gene Selection For Cancer Classification Using Wrapper Approaches
- Classification and regression trees
- Tissue Classification with Gene Expression Profiles
- Selecting Differentially Expressed Genes from Microarray Experiments
- Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays.
- Searching for differentially expressed gene combinations
- Selection bias in gene extraction on the basis of microarray gene-expression data
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