A feature selection function for the GABi biclustering framework, based on the definition of a bicluster as a block of consistently high values across a submatrix within a binary dataset.
Numeric vector representing a subset of the columns from
A fast feature selection function is vital to the GABi framework of biclustering. In GABi, the bicluster problem is reformulated around the fact that each subset of the columns across a dataset will have one _maximal_ subset of rows that fit a specified pattern, and the submatrix defined by this maximal subset of rows will be the most interesting observation involving that subset of columns. Makes use of
fitnessArgs a list of parameters in the environment of execution of the biclustering function
GABi. Notably, the element
consistency is used to apply a stringency threshold for selecting features (i.e. only those with the proportion of high values across the subset of samples being greater than
Numeric vector representing the features (i.e. rows) from dataset
x representing the maximal bicluster for the solution encoded by
Ed Curry [email protected]
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