Abstract:

To improve the classification performance for minority class in an unbalanced dataset,  an improved AdaBoost algorithm (UnAdaBoost algorithm) for an unbalanced dataset was proposed. This algorithm could make the base classification better in order to raise the classification efficienly for the minority class, while to a certain extent losing the accuracy for the majority class. This algorithm could also ensemble the base classifications to make up loss of accuracy in majority class. The performance for  the minority class could be improved and the accuracy for majority class would not be lost. In this study, the improved NaiveBayes algorithm was the base classification, and the base classifiers were fused by the AdaBoost algorithm with improved weight for voting. Experimental results showed that the UnAdaBoost algorithm was effective for an unbalanced dataset compared with the AdaBoost algorithm.
 imbalanced class; AdaBoost algorithm; accuracy

Key words:  imbalanced class, AdaBoost algorithm, accuracy