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            Author: Barnan Das

            Email: barnandas@wsu.edu

            Homepage: www.eecs.wsu.edu/~bdas1

            Last Updated: June 25, 2012

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Description of Algorithm:

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.

Description of Implementation:

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.

Files:

weka.jar -> Weka jar file that is called by several Matlab scripts in this 

        directory.

train.arff, test.arff, resampled.arff -> ARFF (Weka compatible) files generated

                     by some of the Matlab scripts.

ARFFheader.txt -> Defines the ARFF header for the data file "data.csv". Please

          refer to the following link to learn more about ARFF format.

          http://www.cs.waikato.ac.nz/ml/weka/arff.html 

SMOTEBoost.m -> Matlab script that implements the SMOTEBoost algorithm. Please

        "help SMOTEBoost" in Matlab Console to the arguments for this 

        function.

Test.m -> Matlab script that shows a sample code to use SMOTEBoost function in

      Matlab.

ClassifierTrain.m, ClassifierPredict.m, CSVtoARFF.m -> Matlab functions used by 

                               SMOTEBoost.m

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