sequentialForwardSelection {FSinR}

Sequential Forward Selection

Description

Generates a search function based on sequential forward selection. This function is called internally within the searchAlgorithm function. The SFS method (Whitney 1971-sep) starts with an empty set of features and add a single feature at each step with a view to improving the evaluation of the set.

Usage

sequentialForwardSelection(stopCriterion = -1, stop = FALSE)

Arguments

stopCriterion

Define a maximum number of iterations. Disabled if the value is -1 (default: -1 )

stop

If true, the function stops if next iteration does not improve current results (default: FALSE)

Value

Returns a search function that is used to guide the feature selection process.

Author(s)

Adan M. Rodriguez

Alfonso Jiménez-Vílchez

Francisco Aragón Royón

References

Whitney AW (1971-sep). “A Direct Method of Nonparametric Measurement Selection.” IEEE Trans. Comput., 20, 1100–1103. doi: 10.1109/T-C.1971.223410.

Examples

## Not run:  

## The direct application of this function is an advanced use that consists of using this 
# function directly and performing a search process in a feature space
## Classification problem

# Generates the filter evaluation function with sfs
filter_evaluator <- filterEvaluator('determinationCoefficient')

# Generates the search function
sfs_search <- sequentialForwardSelection()
# Performs the search process directly (parameters: dataset, target variable and evaluator)
sfs_search(iris, 'Species', filter_evaluator)

## End(Not run)