Abstract: The existing microRNA prediction methods often present the problems of imbalance data set class and single applicable species.In order to solve the above problems,the main work is as follows.Firstly,a hierarchical sampling algorithm based on sequence entropy was proposed,which can generate a training set enhancing balance positive and negative samples based on the overall distribution of the samples.Secondly,a feature selection algorithm based on signal-to-noise ratio and correlation was designed to reduce the scale of training set and achieve the purpose of improving training speed.Thirdly,the DS-GA was proposed to shorten the optimization time of SVM classifier parameters and avoid the over-fitting problem.At last,based on the idea of ensemble learning,a common microRNA prediction model was established by sampling,feature selection and classifier parameter optimization.Experiments show that the model solves the problem of imbalance effectively,it is not limited to a single species and achieves better results for the hybrid species test set prediction.

Key words: MicroRNA,Prediction,Sampling,Feature selection,Imbalance class