Noise cleaning wrapper

Noise cleaning wrapper

clean_noise(dataset, method, class_attr = "Class", ...)

Arguments

dataset	we want to clean noisy instances on
method	selected method of noise cleaning
class_attr	character. Indicates the class attribute or attributes from dataset. Must exist in it.
...	Further arguments for method

Value

The treated dataset (either with noisy instances replaced or erased)

Examples

library("smartdata")
data(iris0, package = "imbalance")

super_iris <- clean_noise(iris, method = "AENN", class_attr = "Species", k = 3)
super_iris <- clean_noise(iris, "GE", class_attr = "Species", k = 5, relabel_th = 2)
super_iris <- clean_noise(iris, "HARF", class_attr = "Species",
                          num_folds = 10, agree_level = 0.7, num_trees = 5)

# \donttest{
super_iris <- clean_noise(iris0, "TomekLinks")
super_iris <- clean_noise(iris, "hybrid", class_attr = "Species",
                          consensus = FALSE, action = "repair")
super_iris <- clean_noise(iris, "Mode", class_attr = "Species", type = "iterative",
                          action = "repair", epsilon = 0.05,
                          num_iterations = 200, alpha = 1, beta = 1)
super_iris <- clean_noise(iris, "INFFC", class_attr = "Species", consensus = FALSE,
                          prob_noisy = 0.2, num_iterations = 3, k = 5, threshold = 0)
#> Iteration 1: 4 noisy instances removed 
#> Iteration 2: 0 noisy instances removed 
#> Iteration 3: 0 noisy instances removed 
super_iris <- clean_noise(iris, "IPF", class_attr = "Species", consensus = FALSE,
                          num_folds = 3, prob_noisy = 0.2,
                          prob_good = 0.5, num_iterations = 3)
#> Iteration 1: 2 noisy instances removed 
#> Iteration 2: 1 noisy instances removed 
#> Iteration 3: 0 noisy instances removed 
super_iris <- clean_noise(iris, "ORBoost", class_attr = "Species",
                          num_boosting = 20, threshold = 11, num_adaboost = 20)
#> Iteration 1: 0 noisy instances removed.
#> Iteration 2: 6 noisy instances removed.
#> Iteration 3: 0 noisy instances removed.
#> Iteration 4: 0 noisy instances removed.
#> Iteration 5: 0 noisy instances removed.
#> Iteration 6: 0 noisy instances removed.
#> Iteration 7: 0 noisy instances removed.
#> Iteration 8: 0 noisy instances removed.
#> Iteration 9: 0 noisy instances removed.
#> Iteration 10: 0 noisy instances removed.
#> Iteration 11: 0 noisy instances removed.
#> Iteration 12: 0 noisy instances removed.
#> Iteration 13: 0 noisy instances removed.
#> Iteration 14: 0 noisy instances removed.
#> Iteration 15: 0 noisy instances removed.
#> Iteration 16: 0 noisy instances removed.
#> Iteration 17: 0 noisy instances removed.
#> Iteration 18: 0 noisy instances removed.
#> Iteration 19: 0 noisy instances removed.
#> Iteration 20: 0 noisy instances removed.
super_iris <- clean_noise(iris, "PF", class_attr = "Species", prob_noisy = 0.01,
                          num_iterations = 5, prob_good = 0.5, theta = 0.8)
#> Iteration 1: 0 noisy instances removed
#> Iteration 2: 0 noisy instances removed
#> Iteration 3: 0 noisy instances removed
#> Iteration 4: 0 noisy instances removed
#> Iteration 5: 0 noisy instances removed
super_iris <- clean_noise(iris, "C45robust", class_attr = "Species", num_folds = 5)
#> Iteration 1: 3 instances removed
#> Iteration 2: 0 instances removed
#> Summary: 3 instances removed in 2 iterations
# }