Imbalanced Data Classification Based on MBCDK-means Undersampling and GA-ANN

The imbalanced classification problem is often a problem in classification tasks where one class contains a few samples while the other contains a great deal of samples. When the traditional machine learning classification method is applied to the imbalanced data set, the classification performance is bad and the time cost is high. As a result, mini batch with cluster distribution K-means (MBCDK-means) undersampling method and GA-ANN model is proposed in this paper to solve these two problems. MBCDK-means chooses the samples according to the clusters distribution and the distance from the majority class clusters to the minority class cluster center. This technology can keep the original distribution of cluster and increase the sampling rate of boundary samples. It is helpful to improve the final classification performance. At the same time, compared with the classic K-means clustering undersampling method, the presented MBCDK-means undersampling method has lower time complexity. Artificial neural network (ANN) is widely used in data classification but it is easily trapped in a local minimum. Genetic algorithm artificial neural network (GA-ANN), which uses genetic algorithm to optimize the weight and bias of neural network, is raised because of this. GA-ANN achieves better performance than ANN. Experimental results on 8 data sets show the effectiveness of the proposed algorithm.