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+******************************************************************************
+			Author: Barnan Das
+			Email: barnandas@wsu.edu
+			Homepage: www.eecs.wsu.edu/~bdas1
+			Last Updated: June 25, 2012
+******************************************************************************
+
+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
+
+
+**************************************xxx**************************************
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