SMOTEBoost

Implementation of SMOTEBoost algorithm used to handle class imbalance problem in data.

This code implements SMOTEBoost. SMOTEBoost is an algorithm to handle class imbalance problem in data with discrete class labels. It uses a combination of SMOTE and the standard boosting procedure AdaBoost to better model the minority class by providing the learner not only with the minority class examples that were misclassified in the previous boosting iteration but also with broader representation of those instances (achieved by SMOTE). Since boosting algorithms give equal weight to all misclassified examples and sample from a
pool of data that predominantly consists of majority class, subsequent sampling
of the training set is still skewed towards the majority class. Thus, to reduce the bias inherent in the learning procedure due to class imbalance and to
increase the sampling weights of minority class, SMOTE is introduced at each
round of boosting. Introduction of SMOTE increases the number of minority class
samples for the learner and focus on these cases in the distribution at each
boosting round. In addition to maximizing the margin for the skewed class dataset, this procedure also increases the diversity among the classifiers in the ensemble because at each iteration a different set of synthetic samples are
produced.

For more detail on the theoretical description of the algorithm please refer to the following paper:
N.V. Chawla, A.Lazarevic, L.O. Hall, K. Bowyer, "SMOTEBoost: Improving Prediction of Minority Class in Boosting, Journal of Knowledge Discovery in Databases: PKDD, 2003.

The current implementation of SMOTEBoost has been independently done by the author
for the purpose of research. In order to enable the users use a lot of different
weak learners for boosting, an interface is created with Weka API. Currently,
four Weka algortihms could be used as weak learner: J48, SMO, IBk, Logistic.