Class imbalance problem Prabhant Singh & Junaid Ahmed 

Class imbalance problem ● Majority class ○ Far more examples ● Minority class ○ Far less examples ● Level of imbalance ○ Number of Majority classes example / Number of Minority examples 

Class imbalance problem ● Can’t achieve high level accuracy when there is class imbalance ○ Majority dominates the dataset ○ Even small level of imbalance make a difference ● When Learning process suffer due to imbalanced data set ○ Minority classes are more important or can’t be sacrifice 

Class imbalance problem Class imbalance problem refers to the scenario when the data is imbalanced ● Minority classes are important ● Minority class has higher cost than the majority class 

Class imbalance problem Strategies to counter Class imbalance problem ● Undersampling ○ Decreases the majority class examples ■ may lose useful information. ● Oversampling ○ Increases the minority class examples ■ Overfitting 

Class imbalance problem To Improve under sampling: selectively remove the majority class examples such that more informative examples are kept. ● One sided sampling ○ Initial subset contains all the examples ○ Randomly remove the example from subset ○ Constructed 1-NN classifier to classify 

Class imbalance problem To Improve Over sampling Some method use synthetic examples instead of exact copies to reduce the risk of overfitting ● SMOTE ○ Select random example ○ Select one of its neighbors ○ Create a new example 

Performance Evaluation with Class Imbalance ● AUC ● ROC ● F-measure ● G-mean ● Precision recall curve ○ Precision ○ Recall ● 

Performance Evaluation with Class Imbalance ROC ● Receiver Operating characteristic. ● True positive rate(TPR) on the y-axis ● the false positive rate(FPR) on the x-axis. AUC ● Area under curve 

Performance Evaluation with Class Imbalance G-mean or Geometric mean ● Geometric mean of the accuracy of each class ● Good candidate for evaluating class-imbalance learning performance 

Performance Evaluation with Class Imbalance Precision Examples classified as positive are really positive Recall Positive examples are correctly classified as positive 

Performance Evaluation with Class Imbalance F-measure F-measure is defined as the harmonic mean of precision and recall ● Precision does not contain any information about F N ● Recall does not contain any information about F P 

Ensemble Methods for Class-Imbalance Learning 

Data preprocessing methods ● Random Under and over sampling ● SMOTE: Synthetic minority oversampling technique ● MSMOTE ● SPIDER: Selective preprocessing of imbalanced data 

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Addressing Class Imbalance Problem With Classifier Ensembles ● Cost sensitive boosting ensembles ● Boosting based ensembles ● Bagging based ensembles ● Hybrid 

Cost sensitive ensembles ● Adacost ● CSB ● RareBoost ● Ada C1,C2,C3 (covered in previous lectures) 

Boosting based ensembles ● SMOTEBoost ● RUSBoost 

SMOTEBoost ● Just like Adaboost ● Use SMOTE for preprocessing ● Algorithm ○ Generating synthetic examples in each iteration ○ Training classifier on new data ○ Computing the loss of new classifier ○ Update the weights(adaboost) ● Advantages: More diversity to the data 

RUSBoost ● Performs similarly to SMOTEBoost ● Removes instances from the majority class by random undersampling the data in each iteration. ● Not necessarily assigns new weights to the instances. 

Bagging based ensembles ● Underbagging ● Overbagging ● Underoverbagging 

Hybrid Ensembles ● Easy Ensembles ● Balance Cascade 

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