The problem of misclassification of minority class samples appears frequently when classifying massive amount of imbalanced data in real life with traditional classification algorithms, because most of these algorithms only suit balanced class distribution or samples with same misclassification cost. To overcome this problem, a classification algorithm for imbalanced dataset based on cost sensitive ensemble learning and oversampling-New Imbalanced Boost (NIBoost) was proposed. Firstly, the oversampling algorithm was used to add a certain number of minority samples to balance the dataset in each iteration, and the classifier was trained on the new dataset. Secondly, the classifier was used to classify the dataset to obtain the predicted class label of each sample and the classification error rate of the classifier. Finally, the weight coefficient of the classifier and new weight of each sample were calculated according to the classification error rate and the predicted class labeles. Experimental results on UCI datasets with decision tree and Naive Bayesian used as weak classifier algorithm show that when decision tree was used as the base classifier of NIBoost, compared with RareBoost algorithm, the F-value is increased up to 5.91 percentage points, the G-mean is increased up to 7.44 percentage points, and the AUC is increased up to 4.38 percentage points. The experimental results show that the proposed algorithm has advantages on imbalanced data classification problem.