MIAC: MutualInformation Classifier with ADASYN for Imbalanced Classification currently, classification of imbalanced data is a significant issue in the area of data mining and machine learning because of the imbalance of most of the data set. An effective solution of this problem is CostSensitive Learning (CSL), but when the costs are not given, this method cannot work property. As a CostFree Learning (CFL) method, MutualInformation Classification (MIC) can obtain the optimal classification results when the cost information is not given. But this method emphasizes the data of minority class too much and neglects the accuracy of the classification of majority class. And based on the above, this paper presented a CFL method called MutualInformationADASYN Classification (MIAC). Firstly, we get the abstaining samples which are hard to be classified by using MIC. Then we use these abstention samples to synthesize new instance by using the method of ADASYN. Thirdly, we build Mutual InformationADASYN Classification using the new samples. Finally, we use our classifier to get the final results. We evaluated the performance of MIAC on several imbalance binary datasets with different imbalance ratios. The experimental results indicate that the MIAC is more effective than MIC on dealing with imbalanced datasets.

MultiWindow Based Ensemble Learning for Classification of Imbalanced Streaming Data Imbalanced streaming data is widely existed in real world and has attracted much attention in recent years. Most studies focus on either imbalance data or streaming data; however, both imbalance data and streaming data are always accompanied in practice. In this paper, we propose a multiwindow based ensemble learning (MWEL as short) method for the classification of imbalanced streaming data. Three types of windows are defined to store the current batch of instances, the latest minority instances and the ensemble classifier. The ensemble classifier consists of a set of latest subclassifiers, and instances each subclassifier trained on respectively. All subclassifiers are weighted before predicting new arriving instancexe2x80x99s class labels and new subclassifiers are trained if a precision is below a threshold. Extensive experiments on synthetic datasets and real world datasets demonstrate that the new approach can efficiently and efficiently classify imbalanced streaming data and outperform existing approaches.