ebmc (version 1.0.1)

ub: Implementation of UnderBagging

Description

The function implements UnderBagging for binary classification. It returns a list of weak learners that are built on random under-sampled training-sets. They together consist the ensemble model.

Usage

ub(formula, data, size, alg, ir = 1, rf.ntree = 50, svm.ker = "radial")

Arguments

formula

A formula specify predictors and target variable. Target variable should be a factor of 0 and 1. Predictors can be either numerical and categorical.

data

A data frame used for training the model, i.e. training set.

size

Ensemble size, i.e. number of weak learners in the ensemble model.

alg

The learning algorithm used to train weak learners in the ensemble model. cart, c50, rf, nb, and svm are available. Please see Details for more information.

ir

Imbalance ratio. Specifying how many times the under-sampled majority instances are over minority instances. Interger is not required and so such as ir = 1.5 is allowed.

rf.ntree

Number of decision trees in each forest of the ensemble model when using rf (Random Forest) as base learner. Integer is required.

svm.ker

Specifying kernel function when using svm as base algorithm. Four options are available: linear, polynomial, radial, and sigmoid. Default is radial. Equivalent to that in e1071::svm().

Details

UnderBagging uses random under-sampling to reduce majority instances in each bag of Bagging in order to rebalance class distribution. A 1:1 under-sampling ratio (i.e. equal numbers of majority and minority instances) is set as default.

The function requires the target varible to be a factor of 0 and 1, where 1 indicates minority while 0 indicates majority instances. Only binary classification is implemented in this version.

Argument alg specifies the learning algorithm used to train weak learners within the ensemble model. Totally five algorithms are implemented: cart (Classification and Regression Tree), c50 (C5.0 Decision Tree), rf (Random Forest), nb (Naive Bayes), and svm (Support Vector Machine). When using Random Forest as base learner, the ensemble model is consisted of forests and each forest contains a number of trees.

ir refers to the intended imbalance ratio of training sets for manipulation. With ir = 1 (default), the numbers of majority and minority instances are equal after class rebalancing. With ir = 2, the number of majority instances is twice of that of minority instances. Interger is not required and so such as ir = 1.5 is allowed.

The object class of returned list is defined as modelBag, which can be directly passed to predict() for predicting test instances.

References

Barandela, R., Sanchez, J., and Valdovinos, R. 2003. New Applications of Ensembles of Classifiers. Pattern Analysis and Applications. 6(3), pp. 245-256.

Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., and Herrera, F. 2012. A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews). 42(4), pp. 463-484.

Examples

# NOT RUN {
data("iris")
iris <- iris[1:70, ]
iris$Species <- factor(iris$Species, levels = c("setosa", "versicolor"), labels = c("0", "1"))
model1 <- ub(Species ~ ., data = iris, size = 10, alg = "c50", ir = 1)
model2 <- ub(Species ~ ., data = iris, size = 20, alg = "rf", ir = 1, rf.ntree = 100)
model3 <- ub(Species ~ ., data = iris, size = 40, alg = "svm", ir = 1, svm.ker = "sigmoid")
# }