UBL (version 0.0.3)

SmoteClassif: SMOTE algorithm for unbalanced classification problems

Description

This function handles unbalanced classification problems using the SMOTE method. Namely, it can generate a new "SMOTEd" data set that addresses the class unbalance problem.

Usage

SmoteClassif(form, dat, C.perc = "balance", k = 5, repl = FALSE,
             dist = "Euclidean", p = 2)

Arguments

form

A formula describing the prediction problem

dat

A data frame containing the original (unbalanced) data set

C.perc

A named list containing the percentage(s) of under- or/and over-sampling to apply to each class. The over-sampling percentage means that the examples above the threshold are increased by this percentage. The under-sampling percent

k

A number indicating the number of nearest neighbors that are used to generate the new examples of the minority class(es).

repl

A boolean value controlling the possibility of having repetition of examples when performing under-sampling by selecting among the majority class(es) examples.

dist

A character string indicating which distance metric to use when determining the k nearest neighbors.

p

A number indicating the value of p if the "p-norm" distance is chosen.

Value

• The function returns a data frame with the new data set resulting from the application of the SMOTE algorithm.

Details

Unbalanced classification problems cause problems to many learning algorithms. These problems are characterized by the uneven proportion of cases that are available for each class of the problem. SMOTE (Chawla et. al. 2002) is a well-known algorithm to fight this problem. The general idea of this method is to artificially generate new examples of the minority class using the nearest neighbors of these cases. Furthermore, the majority class examples are also under-sampled, leading to a more balanced dataset.

The parameter C.perc controls the amount of over-sampling and under-sampling applied and can be automatically estimated either to balance or invert the distribution of examples across the different classes. The parameter k controls the number of neighbors used to generate new synthetic examples.

References

Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. (2002). Smote: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16:321-357.

See Also

RandUnderClassif, RandOverClassif

Examples

## A small example with a data set created artificially from the IRIS
## data 
data(iris)
dat <- iris[, c(1, 2, 5)]
dat$Species <- factor(ifelse(dat$Species == "setosa", "rare", "common")) 
## checking the class distribution of this artificial data set
table(dat$Species)

## now using SMOTE to create a more "balanced problem"
newData <- SmoteClassif(Species ~ ., dat, C.perc = list(common = 1,rare = 6))
table(newData$Species)

## Checking visually the created data
par(mfrow = c(1, 2))
plot(dat[, 1], dat[, 2], pch = 19 + as.integer(dat[, 3]),
     main = "Original Data")
plot(newData[, 1], newData[, 2], pch = 19 + as.integer(newData[, 3]),
     main = "SMOTE'd Data")


# automatically balancing the data maintaining the total number of examples
datBal <- SmoteClassif(Species ~ ., dat, C.perc = "balance")
table(datBal$Species)

# automatically inverting the original distribution of examples 
datExt <- SmoteClassif(Species ~ ., dat, C.perc = "extreme")
table(datExt$Species)


 library(DMwR)
 data(algae)
 clean.algae <- algae[complete.cases(algae),]
 C.perc = list(autumn = 2, summer = 1.5, winter = 0.9) 
 # class spring remains unchanged
 # In this case it is necessary to define a distance function that 
 # is able to deal with both nominal and numeric features 
 mysmote.algae <- SmoteClassif(season~., clean.algae, C.perc, dist = "HEOM")
 # the distance function may be HVDM 
 smoteBalan.algae <- SmoteClassif(season~., clean.algae, "balance",
                                  dist = "HVDM")
 smoteExtre.algae <- SmoteClassif(season~., clean.algae, "extreme",
                                  dist = "HVDM")

  library(MASS)
  data(cats)
  mysmote.cats <- SmoteClassif(Sex~., cats, list(M = 0.8, F = 1.8))
  smoteBalan.cats <- SmoteClassif(Sex~., cats, "balance")
  smoteExtre.cats <- SmoteClassif(Sex~., cats, "extreme")