ROSE {ROSE}

Generation of synthetic data by Randomly Over Sampling Examples (ROSE)

Description

Creates a sample of synthetic data by enlarging the features space of minority and majority class examples. Operationally, the new examples are drawn from a conditional kernel density estimate of the two classes, as described in Menardi and Torelli (2013).

Usage

 
ROSE(formula, data, N, p=0.5, hmult.majo=1, hmult.mino=1, 
     subset=options("subset")$subset,
     na.action=options("na.action")$na.action, seed)

Arguments

formula

An object of class formula (or one that can be coerced to that class). The left-hand-side (response) should be a vector specifying the class labels. The right-hand-side should be a series of vectors with the predictors. See “Warning” for information about interaction among predictors or their transformations.

data

An optional data frame, list or environment (or object coercible to a data frame by as.data.frame) in which to preferentially interpret “formula”. If not specified, the variables are taken from “environment(formula)”.

N

The desired sample size of the resulting data set generated by ROSE. If missing, it is set equal to the length of the response variable in formula.

p

The probability of the minority class examples in the resulting data set generated by ROSE.

hmult.majo

Optional shrink factor to be multiplied by the smoothing parameters to estimate the conditional kernel density of the majority class. See “References” and “Details”.

hmult.mino

Optional shrink factor to be multiplied by the smoothing parameters to estimate the conditional kernel density of the minority class. See “References” and “Details”.

subset

An optional vector specifying a subset of observations to be used in the sampling process. The default is set by the subset setting of options.

na.action

A function which indicates what should happen when the data contain 'NA's. The default is set by the na.action setting of options.

seed

A single value, interpreted as an integer, recommended to specify seeds and keep trace of the generated sample.

Details

ROSE (Random Over-Sampling Examples) aids the task of binary classification in the presence of rare classes. It produces a synthetic, possibly balanced, sample of data simulated according to a smoothed-bootstrap approach.

Denoted by y the binary response and by x a vector of numeric predictors observed on n subjects i, (i=1, \ldots, n), syntethic examples with class label k, (k=0, 1) are generated from a kernel estimate of the conditional density f(x|y = k). The kernel is a Normal product function centered at each of the x_i with diagonal covariance matrix H_k. Here, H_k is the asymptotically optimal smoothing matrix under the assumption of multivariate normality. See “References” below and further references therein.

Essentially, ROSE selects an observation belonging to the class k and generates new examples in its neighbourhood, where the width of the neighbourhood is determined by H_k. The user is allowed to shrink H_k by varying arguments h.mult.majo and h.mult.mino. Balancement is regulated by argument p, i.e. the probability of generating examples from class k=1.

As they stand, kernel-based methods may be applied to continuous data only. However, as ROSE includes combination of over and under-sampling as a special case when H_k tend to zero, the assumption of continuity may be circumvented by using a degenerate kernel distribution to draw synthetic categorical examples. Basically, if the j-th component of x_i is categorical, a syntehic clone of x_i will have as j-th component the same value of the j-th component of x_i.

Value

The value is an object of class ROSE which has components

Call

The matched call.

method

The method used to balance the sample. The only possible choice is
ROSE.

data

An object of class data.frame containing new examples generated by ROSE.

Warning

The purpose of ROSE is to generate new synthetic examples in the features space. The use of formula is intended solely to distinguish the response variable from the predictors. Hence, formula must not be confused with the one supplied to fit a classifier in which the specification of either tranformations or interactions among variables may be sensible/necessary. In the current version ROSE discards possible interactions and transformations of predictors specified in formula automatically. The automatic parsing of formula is able to manage virtually all cases on which it has been tested it but the user is warned to use caution in the specification of entangled functions of predictors. Any report about possible malfunctioning of the parsing mechanism is welcome.

References

Lunardon, N., Menardi, G., and Torelli, N. (2014). ROSE: a Package for Binary Imbalanced Learning. R Jorunal, 6:82–92.

Menardi, G. and Torelli, N. (2014). Training and assessing classification rules with imbalanced data. Data Mining and Knowledge Discovery, 28:92–122.

See Also

ovun.sample, ROSE.eval.

Examples

# 2-dimensional example
# loading data
data(hacide)

# imbalance on training set
table(hacide.train$cls)
#imbalance on test set
table(hacide.test$cls)

# plot unbalanced data highlighting the majority and 
# minority class examples.
par(mfrow=c(1,2))
plot(hacide.train[, 2:3], main="Unbalanced data", xlim=c(-4,4),
     ylim=c(-4,4), col=as.numeric(hacide.train$cls), pch=20)
legend("topleft", c("Majority class","Minority class"), pch=20, col=1:2)

# model estimation using logistic regression
fit <- glm(cls~., data=hacide.train, family="binomial")
# prediction using test set
pred <- predict(fit, newdata=hacide.test)
roc.curve(hacide.test$cls, pred,
          main="ROC curve \n (Half circle depleted data)")

# generating data according to ROSE: p=0.5 as default
data.rose <- ROSE(cls~., data=hacide.train, seed=3)$data
table(data.rose$cls)

par(mfrow=c(1,2))
# plot new data generated by ROSE highlighting the 
# majority and minority class examples.
plot(data.rose[, 2:3], main="Balanced data by ROSE",
     xlim=c(-6,6), ylim=c(-6,6), col=as.numeric(data.rose$cls), pch=20)
legend("topleft", c("Majority class","Minority class"), pch=20, col=1:2)

fit.rose <- glm(cls~., data=data.rose, family="binomial")
pred.rose <- predict(fit.rose, data=data.rose, type="response")
roc.curve(data.rose$cls, pred.rose, 
          main="ROC curve \n (Half circle depleted data balanced by ROSE)")
par(mfrow=c(1,1))