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License: GNU General Public License v2.0
Home Page: https://ncordon.github.io/imbalance
License: GNU General Public License v2.0
The documentation of the rwo
function claims it can handle every type of dataset. Indeed, the 2nd part of the method adds noise to numeric features and samples from the existing values for all other types of features. However, it can fail earlier because it attempts to convert all features/columns to numeric (which IMO is not necessary in this algorithm, but probably just copy-pasted, as it also exists at the beginning of the other oversampling methods [and makes sense there]). For example, if we have a factor which has non-numeric levels, rwo
throws an error when executing dataset <- toNumeric(dataset, exclude = classAttr)
.
# Does work
imbalance::rwo(data.frame(test1 = rnorm(10), test2 = rnorm(10), class = factor(sample(c("a", "b"), 10, T))), numInstances = 5, classAttr = "class")
# Error
imbalance::rwo(data.frame(test1 = rnorm(10), test2 = factor(sample(c("a", "b"), 10, T)), class = factor(sample(c("a", "b"), 10, T))), numInstances = 5, classAttr = "class")
Checking with cars
data:
Error in check.data(x, allowed.types = c(discrete.data.types, continuous.data.types)) :
variable safety must have at least two levels.
Current build on CRAN is showing problems with Solaris compatibility with respect to the use of C++ calls such as:
sqrt(int)
Documented in https://cran.r-project.org/doc/manuals/r-patched/R-exts.html#Portable-C-and-C_002b_002b-code
Sample code below to illustrate the issue. Documentation says that ratio can be between 0 and 1. However, oversample() gives an error if the ratio specified is really close to 1, and also error for ratio = 1. How can a user work around this?
newDataset <- oversample(glass0, ratio = 0.9, method = "SMOTE") #No problem
newDataset <- oversample(glass0, ratio = 1, method = "SMOTE")
Error in sample.int(length(x), size, replace, prob) :
cannot take a sample larger than the population when 'replace = FALSE'
newDataset <- oversample(glass0, ratio = 1.0, method = "SMOTE")
Error in sample.int(length(x), size, replace, prob) :
cannot take a sample larger than the population when 'replace = FALSE'
newDataset <- oversample(glass0, ratio = 0.95, method = "SMOTE")
newDataset <- oversample(glass0, ratio = 0.99, method = "SMOTE")
Error in sample.int(length(x), size, replace, prob) :
cannot take a sample larger than the population when 'replace = FALSE'
newDataset <- oversample(glass0, ratio = 0.98, method = "SMOTE")
Error in sample.int(length(x), size, replace, prob) :
cannot take a sample larger than the population when 'replace = FALSE'
newDataset <- oversample(glass0, ratio = 0.97, method = "SMOTE") #No error for ratio = 0.97
In racog
the new generated samples are being returned as list, whereas in rwo are been returned as a data.frame
.
Currently a naive mechanism is being used, provided that we know that best bandwidth is going to be O(Silverman's rule of thumb).
for(double v = 0.25; v < 1.5; v = v + 0.05){
possible_bwidth.push_back(v * silverman_bandwidth);
}
Find a better way to adapt the parameter. Maybe a simulated annealing (?).
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