Calculates a Fuzzy Pattern for each category of the samples

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Description

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’).

Usage

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

Arguments

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:

  1. ‘Low’, ‘Medium’, ‘High’.

  2. ‘Low’, ‘Low-Medium’, ‘Medium’, ‘Medium-High’, ‘High’.

  3. ‘Low’, ‘Low-Medium’, ‘Low-Medium-High’, ‘Medium’, ‘Medium-High’, ‘High’.

Default value = 2.

Value

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

Author(s)

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

References

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