Description: 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
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File list (Check if you may need any files):
SMOTEBoost
..........\ARFFheader.txt
..........\ClassifierPredict.m
..........\ClassifierTrain.m
..........\CSVtoARFF.m
..........\data.csv
..........\README.txt
..........\SMOTEBoost.m
..........\Test.m
..........\weka.jar
license.txt
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