中文摘要：

动态数据存在数据量动态改变,数据类别分布非平衡、不稳定等问题,这些问题成为分类的难点.针对该问题,通过对在线极端学习机模型进行拓展,提出鲁棒的权值在线极端学习机算法.为解决动态数据非平衡性,该算法借助代价敏感学习理论生成局部动态权值矩阵,从而优化分类模型产生的经验风险.同时,算法进一步考虑动态数据由于时序性质改变造成的数据分布变化,而引入遗忘因子增强分类器对数据分布变更的敏感性.算法在不同数据分布的24个非平衡动态数据集上测试,取得了较好的效果.

英文摘要：

With the coming of big data age,dynamic data has gradually appeared in various application fields,such as safety monitoring,financial forecasting,and medical diagnostics.Although existing knowledge discovery and data mining techniques have shown great success in many real-world applications,dynamic data has the features of imbalance and instability of data classes,the dynamic change of data volume,which makes it difficult for the classification of dynamic data.To solve these problems,in this paper a robust weighed online sequential extreme learning machine algorithm（RWOSELM）based on the online sequential extreme learning machine algorithm（OSELM）is presented.RWOSELM generates the local dynamic weighted matrix with the help of cost sensitive learning theory,thereby it optimizes the empirical risk of the classification model.Meanwhile,RWOSELM takes the data distribution changes which are caused by temporal properties change of dynamic data into consideration,thus it introduces the forgetting factor to enhance the sensitivity of the classifier to the change of data distribution.The method is able to deal with the data with imbalanced class distribution,and maintains the good robust on dynamic data.This paper tests on 24 datasets with different distribution,and the results show that RWOSELM gets good results on imbalanced dynamic dataset.