Abstract: Many traditional machine learning methods tend to get biased classifier which leads to lower classification precision for minor class in sequential imbalanced data.To improve the classification accuracy of minor class,a new imbalanced online sequential extreme learning machine based on principal curve was proposed.The core idea of the method is to get balanced samples based on the distribution features of online sequential data,reducing the blindness in the process of synthetic minority,which contains two stages.In offline stage,the principal curve is introduced to establish the distribution model of two kinds of samples.Over-sampling is done by using SMOTE for minor class.Then the membership degree of each sample is set according to the projection distance respectively,and the majority and virtual minor samples are deleted according to the under interval.Then the initial model is established.In online stage,over-sampling is done by using SMOTE for online sequential minor samples,getting the balanced samples according to the under interval.Then network weight is updated dynamically.The proposed algorithm has upper bound of the loss of information through the theoretical proof.The experiment was taken on three UCI datasets and the real-world air pollutant forecasting dataset,which shows that the proposed method outperforms the traditional methods in terms of prediction accuracy and numerical stability.

Key words: Online sequential extreme learning machine,Imbalanced data,Principal curve,Synthetic minority over-sampling