naiveWrapper: Naive feature selection method utilising the rFerns shadow...

In mbq/rFerns: Random Ferns Classifier

Description

Proof-of-concept ensemble of rFerns models, built to stabilise and improve selection based on shadow importance. It employs a super-ensemble of iterations small rFerns forests, each built on a subspace of size attributes, which is selected randomly, but with a higher selection probability for attributes claimed important by previous sub-models. Final selection is a group of attributes which hold a substantial weight at the end of the procedure.

Usage

naiveWrapper(x, y, iterations = 1000, depth = 5, ferns = 100, size = 30)

Arguments

x	Data frame containing attributes; must have unique names and contain only numeric, integer or (ordered) factor columns. Factors must have less than 31 levels. No NA values are permitted.
y	A decision vector. Must a factor of the same length as nrow(X) for ordinary many-label classification, or a logical matrix with each column corresponding to a class for multi-label classification.
iterations	Number of iterations i.e., the number of sub-models built.
depth	The depth of the ferns; must be in 1–16 range. Note that time and memory requirements scale with 2^depth.
ferns	Number of ferns to be build in each sub-model. This should be a small number, around 3-5 times size.
size	Number of attributes considered by each sub-model.

Value

An object of class naiveWrapper, which is a list with the following components:

found	Names of all selected attributes.
weights	Vector of weights indicating the confidence that certain feature is relevant.
timeTaken	Time of computation.
params	Copies of algorithm parameters, iterations, depth, ferns and size, as a named vector.

Author(s)

Miron B. Kursa

References

Kursa MB (2017). Efficient all relevant feature selection with random ferns. In: Kryszkiewicz M., Appice A., Slezak D., Rybinski H., Skowron A., Ras Z. (eds) Foundations of Intelligent Systems. ISMIS 2017. Lecture Notes in Computer Science, vol 10352. Springer, Cham.

Examples

set.seed(77);
#Fetch Iris data
data(iris)
#Extend with random noise
noisyIris<-cbind(iris[,-5],apply(iris[,-5],2,sample));
names(noisyIris)[5:8]<-sprintf("Nonsense%d",1:4)
#Execute selection
naiveWrapper(noisyIris,iris$Species,iterations=50,ferns=20,size=8)

mbq/rFerns documentation built on May 22, 2019, 12:57 p.m.