分类非平衡数据的SMOTEboost算法-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