sbo: Implementation of SMOTEBoost

Description Usage Arguments Details Value References Examples

View source: R/ebmc.R


The function implements SMOTEBoost for binary classification. It returns a list of weak learners that are built on SMOTE-manipulated training-sets, and a vector of error estimations of each weak learner. The weak learners altogether consist the ensemble model.


sbo(formula, data, size, alg, over = 100, smote.k = 5, rf.ntree = 50, svm.ker = "radial")



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


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


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


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.


Specifying over-sampling rate of SMOTE. Only multiple of 100 is acceptable.


Number of k applied in SMOTE algorithm. Default is 5.


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


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().


Based on AdaBoost.M2, SMOTEBoost uses SMOTE (Synthetic Minority Over-sampling TEchnique) to increase minority instances in each iteration of training weak learners. An over-sampling rate of SMOTE can be defined by users with argument over.

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.

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


The function returns a list containing two elements:


A list of weak learners.


Error estimation of each weak learner. Calculated by using (pseudo_loss + smooth) / (1 - pseudo_loss + smooth). smooth helps prevent error rate = 0 resulted from perfect classfication during trainging iterations. For more information, please see Schapire et al. (1999) Section 4.2.


Chawla, N., Lazarevic, A., Hall, L., and Bowyer, K. 2003. SMOTEBoost: Improving Prediction of the Minority Class in Boosting. In Proceedings European Conference on Principles of Data Mining and Knowledge Discovery. pp. 107-119

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.

Freund, Y. and Schapire, R. 1997. A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences. 55, pp. 119-139.

Freund, Y. and Schapire, R. 1996. Experiments with a new boosting algorithm. Machine Learning: In Proceedings of the 13th International Conference. pp. 148-156

Schapire, R. and Singer, Y. 1999. Improved Boosting Algorithms Using Confidence-rated Predictions. Machine Learning. 37(3). pp. 297-336.


iris <- iris[1:70, ]
iris$Species <- factor(iris$Species, levels = c("setosa", "versicolor"), labels = c("0", "1"))
model1 <- sbo(Species ~ ., data = iris, size = 10, over = 100, alg = "c50")
model2 <- sbo(Species ~ ., data = iris, size = 20, over = 200, alg = "rf", rf.ntree = 100)
model3 <- sbo(Species ~ ., data = iris, size = 40, over = 300, alg = "svm", svm.ker = "sigmoid")

Example output

ebmc documentation built on Jan. 11, 2022, 1:06 a.m.

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