sequentialFloatingForwardSelection {FSinR}

Sequential Floating Forward Selection

Description

Generates a search function based on sequential floating forward selection. This function is called internally within the searchAlgorithm function. The sffs method (Pudil et al. 1994) 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. In addition, it checks whether removing any of the included features, improve the value of the set.

Usage

sequentialFloatingForwardSelection()

Value

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

Author(s)

Adan M. Rodriguez

Francisco Aragón Royón

References

Pudil P, Novovičová J, Kittler J (1994). “Floating search methods in feature selection.” Pattern recognition letters, 15, 1119–1125.

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
filter_evaluator <- filterEvaluator('determinationCoefficient')

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

## End(Not run)