calculateFuzzyPatterns: Calculates a Fuzzy Pattern for each category of the samples
In DFP: Gene Selection

Description Usage Arguments Value Author(s) References

Calculates a Fuzzy Pattern for each category. To do this, a given percentage of the samples belonging to a category must have the same label (‘Low’, ‘Medium’ or ‘High’).

1	calculateFuzzyPatterns(rmadataset, dvs, piVal = 0.9, overlapping = 2)

`rmadataset`	`ExpressionSet` with numeric values containing gene expression values (rows) of samples belonging to different categories (columns). The `ExpressionSet` also contains an `AnnotatedDataFrame` with metadata regarding the classes to which each sample belongs.
`dvs`	Matrix containing discrete values according to the overlapping parameter after discretizing the gene expression values. Includes an attribute `types` which determines the category of each sample.
`piVal`	Controls the degree of exigency for selecting a gene as a member of a Fuzzy Pattern. `Default value = 0.9`. `Range[0,1]`.
`overlapping`	Modifies the number of membership functions used in the discretization process. Possible values: ‘Low’, ‘Medium’, ‘High’. ‘Low’, ‘Low-Medium’, ‘Medium’, ‘Medium-High’, ‘High’. ‘Low’, ‘Low-Medium’, ‘Low-Medium-High’, ‘Medium’, ‘Medium-High’, ‘High’. `Default value = 2`.

Genes belonging to each Fuzzy Patterns. There are one FP for each class.
Includes an attribute ifs with the Impact Factor for each category.

Rodrigo Alvarez-Gonzalez
Daniel Glez-Pena
Fernando Diaz
Florentino Fdez-Riverola
Maintainer: Rodrigo Alvarez-Gonzalez <rodrigo.djv@uvigo.es>

F. Diaz; F. Fdez-Riverola; D. Glez-Pena; J.M. Corchado. Using Fuzzy Patterns for Gene Selection and Data Reduction on Microarray Data. 7th International Conference on Intelligent Data Engineering and Automated Learning: IDEAL 2006, (2006) pp. 1095-1102

DFP documentation built on Nov. 8, 2020, 7:46 p.m.