sequentialBackwardSelection {FSinR}

Sequential Backward Selection

Description

Generates a search function based on sequential backward selection. This function is called internally within the searchAlgorithm function. The SBS method (Marill and Green 1963-02) starts with all the features and removes a single feature at each step with a view to improving the evaluation of the set.

Usage

sequentialBackwardSelection(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

Marill T, Green D (1963-02). “On the effectiveness of receptors in recognition systems.” Information Theory, IEEE Transactions on, 9, 11–17. doi: 10.1109/TIT.1963.1057810.

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 sbs
filter_evaluator <- filterEvaluator('determinationCoefficient')

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

## End(Not run)